Part 2

AI Trends by Domain

Part 2: AI Trends by Domain[Overview]
AI and robotics are currently used in various domains. Part 2 introduces cases primarily inJapan in eight different domains, where AI and robotics are introduced in many organizations—that is, healthcare, elderly care, art and design, education, hospitality, transportation and mobility,
agriculture, and public order and security. This part also introduces how AI is used for military
purposes overseas and in the world of shōgi (Japanese chess) in the column section. Introducing
actual cases in which AI and robotics have already been adopted provides insight for other areasin which AI and robotics will prevail in the future.
In this report, we focus on the employment and labor of “experts” by using AI and robotics
as tools. Therefore, we make only minimal reference to end users, such as patients and consumers.
The experts discussed in Part 2 are, in concrete terms, doctors, care workers (however, we needto keep in mind that family members bear quite a few roles), creators, teachers, those who workin the service industry, drivers, farmers, police officers and security guards, soldiers, and shōgi
players. Bearing in mind that AI will be used in various fields in the future, the report sorts out
how experts in different fields are using AI and robot technologies that are beginning to prevail in
their workplaces.
This report adopts the perspective that technologies are used within interactions with context
or needs, such as social issues. In Part 2, therefore, each chapter (1) describes the backgroundinformation, such as issues facing each domain in Japan, social and industrial structures, andphilosophies; (2) discusses that there are institutional and social measures against these issues, inaddition to technological measures, and (3) introduces the actual cases in which AI, robotics, andICT are used, instead of describing how technologies are used. Finally, each chapter sorts out the
points of argument, such as legal, social, and ethical issues arising from the interaction between
technology and society, and introduce issues that are difficult to solve only with technology.

Ⅰ Healthcare
1. Policy issues in the domain of healthcare
Population aging and birthrate declines are progressing more rapidly in Japan compared with othercountries in the world. According to the National Institute of Population and Social Society Research, thetotal population is decreasing, while the rate of population aging keeps increasing; indeed, about one of four
people in Japan is estimated to be an elderly person aged 75 or older.1 According to the Ministry of Health,* The last date of access to the internet information in this paper is May 1, 2018.
1 National Institute of Population and Social Society Research, Population Projection for Japan: 2016 to 2065 (Institute of Population
Problems research series Vol.336), 2017.7.31, p.230. <http://www.ipss.go.jp/pp-zenkoku/j/zenkoku2017/pp29_ReportALL.pdf>.
(in Japanese).

50Labour and Welfare, the national health expenditure was 42 trillion yen (estimated amount) in 2015 and has
increased every year since then,2 and it is expected to reach 61 trillion yen in 2025.3
Underlying this increase in healthcare expenditure is the increase in the average life expectancy due torecent advancements in medical technologies (particularly acute-phase treatment) and changes in the disease
structure—that is, the increase in the number of three major mortal diseases,4 malignant neoplasm (cancer),heart disease, and cerebrovascular disease, and the increase in the number of chronic diseases, mainly
diabetes. These diseases not only take away life but also often cause a decline in physical functions andquality of life. It has been noted that the development and progress of these diseases are associated withlifestyle habits, including dietary habits, fitness habits, alcohol consumption, smoking, and stress. Therefore,
promoting efforts to improve lifestyle habits on a continuous basis and to improve health is an effectivemeans to prevent them.5
To support the super-aged society, we should secure healthcare professionals, maintain the quality of
medical care, and provide efficient medical services. On the other hand, expectations are placed on theindustries associated with the “extension of healthy life expectancy,” including healthcare and elderly careindustries, as growing industries. In the “New Growth Strategy” (Cabinet decision on June 18, 2010), thehealthcare industry, elderly care industry, and health-related industry are positioned as industries driving
growth in Japan. A plan was also announced to establish a system or framework to provide various products
and services while ensuring safety and improving quality in order to encourage the private sector to enter
into these industries, with the goal of creating a market worth about 50 trillion yen and generating 2.84
million new jobs by 2020. 6 “Growth Strategy 2017” (Cabinet decision on June 9, 2017) addresses the“extension of healthy life expectancy” as one of the five areas on which the national government has focusedpolicy resources, aiming to “substantially improve quality and reduce burdens of doctors and patients”
through the use of telemedicine and telecare as well as artificial intelligence (AI).7
2. Ensuring the availability healthcare professionals and the use AI and robotics(1) Ensuring the availability of medical care professionals and work style reform
An excess of doctors was noted as one of the factors causing an increase in medical care expenditure in
the 1980s. 8 Therefore, measures were taken to reduce the number of university medical students. An
Ministry of Health, Labour and Welfare, “Summary of the National Medical Care Expenditure in 2015,” 2017.9.13, p.12.
<http://www.mhlw.go.jp/toukei/saikin/hw/k-iryohi/15/dl/data.pdf>. (in Japanese).
3 For example, General Affairs Division, Health Insurance Bureau, Ministry of Health, Labour and Welfare, “About Medical
Insurance Reform,” 2015.6.19, p.4.
<http://www.mhlw.go.jp/file/06-Seisakujouhou-12600000-Seisakutoukatsukan/0000089357.pdf>. (in Japanese).According to
National Federation of Health Insurance Societies, “Estimation of National Medical Care Expenditures toward 2025,” 2017.9, p.2.
<http://www.kenporen.com/include/press/2017/20170925_1.pdf> (in Japanese). it is estimated to be 57.8 trillion yen.
4 Of these, the number of deaths from cerebrovascular disease per population in Japan is decreasing due to advancements in acutephase treatment, but there are many patients suffering from the disease as a chronic disease. Therefore, its medical cost expenditure
remained high at about 1.8 trillion yen in 2015 (Ministry of Health, Labour and Welfare, op.cit. (2), p.19).
5 Many epidemiological studies are being conducted globally, including Kyushu University in Hisayama-cho, Fukuoka Prefecture,“Hisayama Study.” < http://www.epi.umn.edu/cvdepi/study-synopsis/hisayama-study/>.
6 “New Growth Strategy: Blueprint for Revitalizing Japan,” (Cabinet decision on June 18, 2010), p.23-25. Office of the Prime
Minister website < https://japan.kantei.go.jp/kan/topics/sinseichou01_e.pdf>
7 “Growth Strategy 2017” (Cabinet decision on June 9, 2017), p.13. Ministry of Foreign Affairs of Japan website <http://www.mofa.go.jp/files/000272312.pdf >.
8 Hitoshi Yoshimura, “My view on situations surrounding medical care expenditure and measures taken,” Social Security Review,
No.1424, 1983.3.11, p.13. (in Japanese).
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appropriate allocation of doctors was also discussed back then, but doctors are now often overworked.9
Doctors work extremely long hours and have a duty to provide treatment.10 However, it is also difficult todistinguish labor from training,11 and it is therefore necessary to establish an environment in which they cancontinue learning though on the on-the-job training (OJT). This is a work practice that forces self-sacrificeand creates uncertainty regarding a future career. Some note that medical treatment has been assured throughthe self-sacrifice of healthcare professionals, including doctors, nurses, and other medical staff.12
Under these circumstances, measures were taken against the shortage of doctors and an uneven
distribution of doctors. Specifically, the number of students accepted to medical schools was increased,13with the establishment of a framework to accept students from the same prefecture. In addition, new medical
departments were established,14 which had been discouraged for many years. Team medicine15 has also
been promoted through an expansion of the work scope of nurses and staff other than doctors. In 2015, thegovernment began promoting the development of nurses16 who could provide some assistance in medicalcare (for example, IVs to those who are in a state of dehydration (by judging the level of dehydration andmaking corrections via transfusion)) using procedure manuals without waiting for the judgment of doctors
or dentists. The government is also looking into reforming work styles based on the actual work situation17of healthcare professionals.18

9There have been incidents and lawsuits associated with overworking doctors, including a trainee doctor at Kansai Medical
University (1998), a pediatrician in Tokyo (1999), a trainee doctor of gynecology and obstetrics in Tokyo (2015), and a trainee
doctor at Niigata City General Hospital.
10 “No medical practitioner who provides medical treatment shall refuse any request for examination or treatment without just cause.”
Medical Practitioners' Act (Act No. 201 of 1948), Article 19 (1).
11 It has been held that “trainee doctors are workers as well,” pertaining to the overwork death of a trainee doctor at Kansai Medical
University in 1998. Decision by the Second Petty Bench of the Supreme Court, Supreme Court Reports (civil cases), Volume 59,
No. 5, p.938. (in Japanese).
12 Committee for examining future work practices of doctors and nurses based on new medical practices, “Report of the Committee
for examining future work practices of doctors and nurses based on new medical practices,” 2017.4.6, p.4, 11. Ministry of Health,Labour and Welfare Website <http://www.mhlw.go.jp/file/05-Shingikai-10801000-Iseikyoku-Soumuka/0000161081.pdf>. (inJapanese). There have been deaths of nurses from overwork, such as at National Cardiovascular Center (2001) and Tokyo SaieseikaiCentral Hospital (2007).
13 Ministry of Education, Culture, Sports, Science and Technology, “About increase of the prescribed number of medical students in
2018,”
2017.10.16.
Ministryof
Education,
Culture,Sports,ScienceandTechnology
Website<http://www.mext.go.jp/component/b_menu/shingi/toushin/__icsFiles/afieldfile/2017/10/16/1397261_2_1.pdf>. (in Japanese).
14 In 2016, the Faculty of Medicine was established at Tohoku Medical and Pharmaceutical University as the first department of
medicine that was newly established for the first time in 38 years. In 2017, the Faculty of Medicine was established at theInternational University of Health and Welfare.
15 Various healthcare professionals engaging in medical care share goals and information, divide tasks but cooperate with andcomplement each other, and provide medical care appropriate for the situation of each patient, assuming that they are highly skilled.
Ministry of Health, Labour and Welfare, “About promotion of team medicine (Report of the committee for investigating promotionof team medicine),” 2010.3.19. <http://www.mhlw.go.jp/shingi/2010/03/dl/s0319-9a.pdf>. (in Japanese).
16 “Summary of the nurse training system to perform specific tasks,” Ministry of Health, Labour and Welfare Website<http://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000070423.html>. (in Japanese).
17 Ministry of Health Labour and Welfare, Special research “Survey of doctors’ working conditions and their opinions on work styles,”Research Group & Health Policy Bureau, “Survey of doctors’ working conditions and their opinions on work styles,” 2017.4.6.
<http://www.mhlw.go.jp/file/05-Shingikai-10801000-Iseikyoku-Soumuka/0000163402.pdf>. (in Japanese). In addition, demandsupply estimations of doctors, nurses, physical therapists, and occupational therapists are discussed at the “Committee forinvestigating demand-supply of healthcare professionals,” Ministry of Health Labour and Welfare Website,
http://www.mhlw.go.jp/stf/shingi/other-isei.html?tid=315093>. (in Japanese).
18 “Committee for investigating work style reform of doctors,” same as above <http://www.mhlw.go.jp/stf/shingi/otherisei.html?tid=469190> (in Japanese).; “Social Security Council (subpanel on medical insurance),” same as above<http://www.mhlw.go.jp/stf/shingi/shingi-hosho.html?tid=126706> (in Japanese).; Fumi Tsutsui, “Work style reform of doctors
proposed by freelance doctor,” Shuzo Nishimura (ed.), White Paper on Healthcare: 2017-2018, Japan Medical Planning, 2017,
pp.145-153. (in Japanese).

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(2) Discussions on the use of AI and robotics in the domain of healthcare
In terms of the use of AI and robotics in the domain of medicine, various possible applications havebeen debated along with the progress of medical informatization. Examples include assisting information
gathered during medical interviews and tests, diagnosis based on gathered information and its assistance,planning treatment programs (including genomic medicine), implementation of treatment (including robots),
drug discovery, improvement of the quality of medical care, streamlining of medical care, reductions of workburdens, generation of new medical knowledge (e.g., disease concepts), prevention and early detection, andmonitoring dementia patients.
On the other hand, some voiced strong objections to the introduction of computers in medicine in the
1970s, because they would take away doctors’ jobs. 19 For instance, developed by Stanford University,“Mycin,” which is an attempt to apply an expert system20 developed in the 1970s to medicine, is a devicefor diagnosing contagious blood diseases and presenting recommended drug treatment. While it showed
better results than non-specialist doctors,21 it was not put to practical use due to ethical and legal issues (e.g.,
liability in the event of an error).22
To improve the quality of medical care, reduce burdens on the medical front, and streamline medical
processes, expectations have been placed on information and communication technology (ICT), including
AI.23 Especially due to advancements in technologies, such as deep learning, possibilities for utilizing AI inthe medical field are growing. The Ministry of Health, Labour and Welfare’s Panel for AI Utilization in
Healthcare presented a policy to promote AI development in the six priority areas, “diagnostic imaging
support,” “drug discovery,” “surgery support,” “genomic medicine,” “diagnosis and treatment support,” and“elderly care and dementia.”24 The ministry’s committee for examining the future work practices of doctors
and nurses based on new medical practices has also encouraged the promotion of technological innovationthat can be utilized on the medical front, such as the adoption of technology such as deep learning in medicalcare, high-accuracy diagnostic imaging using AI, and practical application of patient-watching robots using
sensor technology.25“Healthcare Information Systems,” 2016.4.7, pp.6-7. Graduate School of Medicine and Faculty of Medicine, The University ofTokyo website <http://www.m.u-tokyo.ac.jp/medinfo/wp-content/uploads/2014/10/SPH2016-04-07handoutUP1.pdf>. (inJapanese).
20 AI system that makes computers memorize experts’ knowledge and have them make inferences or solve problems based on it.
21 Bruce G. Buchanan and Edward H. Shortliffe, Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic
Programming Project, Reading, MA: Addison-Wesley, 1984, pp.592-593.
22 See Randolph A. Miller et al., “Ethical and Legal Issues Related to the Use of Computer Programs in Clinical Medicine,” Annals
of Internal Medicine, Vol.102 No.4, 1985.4, pp.529-536 for ethical and legal issues as of 1985 regarding the clinical use of similar
computer programs.
23 Panel for ICT Utilization in Healthcare, “Toward establishment of ‘next-generation healthcare system’ using ICT – ‘Create’‘connect’ and ‘open’ data,” 2016.10.19. Ministry of Health, Labour and Welfare website <http://www.mhlw.go.jp/file/05Shingikai-12601000-Seisakutoukatsukan-Sanjikanshitsu_Shakaihoshoutantou/0000140306.pdf>. (in Japanese).
24 Panel for AI Utilization in Healthcare, “Report of the Panel for AI Utilization in Healthcare,” 2017.6.27. Ministry of Health, Labourand Welfare Website<http://www.mhlw.go.jp/file/05-Shingikai-10601000-Daijinkanboukouseikagakuka-Kouseikagakuka/0000169230.pdf>.
(inJapanese).
25 Committee for examining future work practices of doctors and nurses based on new medical practices, op.cit. (12), p.39.
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3. Cases
Hereinafter, this chapter will go over how AI and robotics are utilized in “diagnostic imaging support,”“genomic medicine,” “diagnosis and treatment support,” and “surgery support” among the six priority areas,
how they affect work styles, and what kind of issues arise.26(1) Diagnostic imaging support
The use of deep learning is highly anticipated in the field of diagnostic imaging support. It is now
clinically used in areas such as radiology, pathology, dermatology, ophthalmology, and endoscopy.27 The
Japan Agency for Medical Research and Development (AMED) has implemented a project for developing
image databases in academic societies of various domains and promoting their use through AI.28
Traditionally, doctors look at these images themselves to make diagnostic judgments, but with the useof AI, many of these tasks of doctors will probably be replaced. However, accuracy above a certain level isrequired of AI to prevent liability from arising from such judgments. Therefore, AI will be utilized for alimited purpose under the responsibility of doctors—that is, for diagnosis support for the time being.29 Yet,
AI has achieved the same level of judgment as regular doctors or even higher.30 Therefore, some note that
“AI won’t replace doctors, but doctors not using AI will be replaced by doctors using AI.”31
26 See “II Elderly Care” for “elderly care and dementia.” This paper will not discuss in detail “drug discovery” because employment

and labor issues arising from the use of AI and robotics are subtle in this area (drug discovery using AI and robotics is anticipatedas part of “genomic medicine.”) Other than the priority areas, AI can be utilized to formulate medical policies, streamline people’s
flow within hospitals, and streamline operations. Some are cautious about the use of AI in public areas (for example, Julia Powles
and Hal Hodson, “Google DeepMind and healthcare in an age of algorithms,” Health and Technology, Vol.7 No.4, 2017.12, pp.351367. <https://link.springer.com/content/pdf/10.1007%2Fs12553-017-0179-1.pdf>).
27 Research cases include the following: Varun Gulshan et al., “Development and Validation of a Deep Learning Algorithm for
Detection of Diabetic Retinopathy in Retinal Fundus Photographs,” JAMA, Vol.316 No.22, 2016.12.13, pp.2402-2410.
<https://jamanetwork.com/journals/jama/fullarticle/2588763>; Andre Esteva et al., “Dermatologist-level classification of skincancer with deep neural networks,” Nature, Vol.542(7639), 2017.1.25, pp.115-118; Daniel Shu Wei Ting et al., “Development andValidation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic
Populations With Diabetes,” JAMA, Vol.318 No.22, 2017.12.12, pp.2211-2223; Satoki Shichijo et al., “Application of
Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images,” EBioMedicine,Vol.25, pp.106-111; Neeraj Kumar et al., “A Dataset and a Technique for Generalized Nuclear Segmentation for Computational
Pathology,” IEEE Transactions on Medical Imaging, Vol.36 No.7, 2017.7, pp.1550-1560; Babak Ehteshami Bejnordi et al.,“Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer,”
JAMA, Vol.318 No.22, 2017.12.12, pp.2199–2210. There also efforts in the private sector, including Enlitic<https://www.enlitic.com/>,Arterys <https://arterys.com/>, Infervision <http://www.infervision.com/infer-en>, and NVIDIA<http://www.nvidia.com/>. There are also articles on the use of AI in endoscopy, including Junichi Oshita, “Is it true endoscope isgetting
smarterwith
AI ? ”
2017.9.25.
NikkeiTechnology
OnlineWebsite<http://techon.nikkeibp.co.jp/atcl/feature/15/030200065/092100010/>. (in Japanese).
28 Image databases are being built at Japan Radiological Society, Japanese Society of Pathology, Japan GastroenterologicalEndoscopy Society, and Japanese Ophthalmological Society. Makoto Suematsu, “Efforts and Issues of the Japan Agency for
Medical Research and Development (collection of attachments) (3rd Council of Research and Management 2017 (November 8,2017) Material 1-2), 2017.11.8, pp.30-31. <https://www.amed.go.jp/content/000024495.pdf>. (in Japanese).As part of the same
study, “Research Center for Medical Big Data” was created within the National Institute of Informatics (NII) to establish cloud
platforms to collect medical imaging big data using an academic information network created and operated by NII called “SINET5”
and to conduct research and development of medical image analysis systems using AI technology. “NII launches new ResearchCenter for Medical Big Data/FDesigning cloud platforms to collect medical imaging data and developing image analysis technology
using AI,” 2017.12.25. National Institute of Informatics website < https://www.nii.ac.jp/en/news/release/2017/1225.html>.
29 Panel for AI Utilization in Healthcare, op.cit. (24), p.29. This concerns only the use of AI in general medical practices and is not
limited to diagnostic imaging support.
30 Esteva et al. has demonstrated the accuracy of the specialist doctor level in terms of the identification of skin cancers using AI(Esteva et al., op.cit.(27)). Bejnordi et al. reports accuracy above the level of doctors in terms of the detection of breast cancer
metastasis (Bejnordi et al., op.cit.(27)).
31 Statement from Curtis Langlotz of Stanford University at the meeting of the Radiological Society of North America (RSNA).
Shaun Sutner, “Radiology AI and deep learning take over RSNA 2017,” 2017.11.28. TechTarget SearchHealthIT Website<http://searchhealthit.techtarget.com/news/450430826/Radiology-AI-and-deep-learning-take-over-RSNA-2017>.

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Trust from experts on programs using AI was considered an issue with “Mycin” mentioned earlier.
However, trust is expected to increase when the accuracy of AI increases. Vast volumes of data arenonetheless necessary to gather and analyze imaging data for learning. Such data are usually gathered
without assuming the use in the development process of programs using AI. Therefore, there is an issue of
how to obtain consent from individuals from the perspective of the protection of personal information. In
addition, the accuracy of these programs using AI keeps changing along with learning, and the decisionmaking process may turn into a so-called “blackbox.” Therefore, there is also an issue of how they shouldbe evaluated as medical equipment.32(2) Genomic medicine
In terms of the use of AI in genomic-based drug discovery and precision medicine,33 there are several
cases in Japan and overseas, such as the use of IBM’s AI called “Watson”34 at the Institute of Medical
Science, University of Tokyo.35 In these cases, AI is applied to data that cannot be handled by people
alone—that is, data of piles of academic papers and biomolecule information such as genomes andepigenomes.36 Therefore, such AI probably will not take away jobs, but it may influence work styles bysignificantly changing the positions of tests, for instance.
It was reported by media that “AI made a diagnosis” at the Institute of Medical Science, University ofTokyo, but the final judgment was actually made by a doctor. Therefore, it is not so different from evidencebased medicine (EBM)37 at least for now. As in this case, when using AI for efficiently analyzing vast

The “21st Century Cures Act” (P.L.114-255) established in the US in 2016 stipulates in “Sec. 3060. Clarifying medical software
regulation” that the Food and Drug Administration (FDA) shall not regulate the software function, including a portion of AI, forsupporting the clinical judgment of a health care professional. In December 2017, the FDA announced a draft guideline forclarifying what should be regulated in terms of software supporting the clinical judgment of a health care professional and aguideline defining the principles when evaluating the safety, effectiveness, and performance of software positioned as medical
equipment that should be regulated by the FDA. “Statement from FDA Commissioner Scott Gottlieb, M.D., on advancing new
digital health policies to encourage innovation, bring efficiency and modernization to regulation,” 2017.12.7. FDA Website<https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm587890.htm>. FDA permitted marketing of the first
artificial intelligence-based device to detect certain diabetes-related eye problems on April 11, 2018, same as above<https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm604357.htm>.
33 Precision medicine refers to the provision of optimal disease prevention and treatment at the personal level of each patient bygiving consideration to genetic information and lifestyles.
34 Satoru Miyano, “About artificial intelligence, the fundamental and key technology for the future of genomic analysis: Current
situations of AI and its future problems,” Japan Health Sciences Foundation. “Latest trends of the use of big data, ICT and AI in
medicine: Exploring a path to make contributions to drug discovery, individualized medicine, and preemptive medicine,” 2017.3,
pp.137-145. <http://www.jhsf.or.jp/paper/report/report_201604.pdf> (in Japanese).; “AI gives advice on cancer treatment,
detecting the type of leukemia,” Nihon Keizai Shimbun. 2016.8.5, p.42. (in Japanese).
35 In “Establishment of a clinical and genomic information and knowledge platform for promoting genomic medicine” selected in2016 for the AMED’s “Program for an Integrated Database of Clinical and Genomic Information”, a genomic database isdesigned on the premise of the use of AI. Katsuyoshi Masuda, “Kyoto University and Fujitsu use AI for an integrated database ofclinical and genomic information,” 2016.10.10. Nikkei Technology Online Website<http://techon.nikkeibp.co.jp/atcl/news/16/101004473/> (in Japanese).; “2016 sponsored research and development report:
Establishment of a clinical and genomic information and knowledge platform for promoting genomic medicine,” 2016.5.31.
Japan Agency for Medical Research and Development Website<https://www.amed.go.jp/content/files/jp/houkoku_h28/0401047/h28_002.pdf> (in Japanese).. In the project “Development of an
integrated cancer treatment system using AI” selected for Japan Science and Technology Agency’s Strategic Basic Research
Program (CREST), an integrated cancer treatment system, including genomics, is being developed. Ryuji Hamamoto,“Development of an integrated cancer treatment system using AI,” 2016.11.29. National Cancer Center Japan Website<https://www.ncc.go.jp/jp/information/pr_release/2016/1129/press_release_20161129_01.pdf> (in Japanese).. Others include acase of Cambridge Cancer Genomics in the UK <http://ccg.ai/>.
36 DNA’s base sequence information is called a genome, whereas other information attached to the genome is called an epigenome.
37 Medicine based on the best scientific grounds (evidence), such as epidemiologic and statistical data.
32

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volumes of data for a purpose similar to EBM, we need to pay attention to how data, which will be used as“evidence”, are gathered and how accurate the diagnoses will be (or what is the levels and grades of evidence
for that). When services in which only AI performs medical practices38 without any intervention of doctors
will be available in the future,39 AI may take away the tasks of doctors.
(3) Diagnosis and treatment support
Multiple cases have been reported in terms of the use of AI for diagnosis and treatment support in fields
other than diagnostic imaging and genomic medicine. It depends on how you define AI, but AED determines
whether a person should receive an electric shock. Therefore, diagnosis and treatment support equipmentusing AI is already prevalent in a broad sense.
In addition to imaging, the use of AI is highly anticipated with text data, such as electronic chart, movie,
and audio data containing expressions and motions, as well as diagnosis and treatment support40 or healthmanagement using daily life data from wearable devices. In addition, various efforts have been made bothin Japan and overseas.41
For example, Keio University is working on the development of a device to provide objective
evaluations of psychiatric symptoms in real time by quantifying facial expressions, voices, and daily life
activities using machine learning.42 This device is still under development. It is a device for supporting theevaluation of severity, and therefore, it does not replace the tasks of doctors but rather reduces the workburden of doctors and psychologists. As in the other cases of the use of AI, the main issues here are the38

According to Article 17 of the Medical Practitioners Act, only doctors can engage in medical practice (practicing medicine withthe will to do so continuously and repeatedly).
39 From 2017, the Ministry of Health, Labour and Welfare is expected to sort out the relationship between Article 17 of the Medical
Practitioners Act and diagnosis and treatment support, where it is unclear whether it is medical consultation or medical practiceby AI. Panel for AI Utilization in Healthcare, op.cit. (24), p.32.
40 The use of AI is also anticipated for improving the clinical effectiveness of treatment and optimization of costs, such as early
discharge support.
41 Cases other than the ones introduced in the body text include the following: Zahi N. Karam et al., “Ecologically valid long-termmood monitoring of individuals with bipolar disorder using speech,” IEEE International Conference on Acoustics, Speech, andSignal Processing (ICASSP), 2014, pp.4858-4862; Colin G. Walsh et al., “Predicting Risk of Suicide Attempts over Time throughMachine Learning,” Clinical Psychological Science, Vol.5, No.3, 2017.5, pp.457-469; Skyler Place et al., “Behavioral Indicators
on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders,” Journal of
Medical Internet Research, Vol.19 No.3, 2017.3. <http://www.jmir.org/2017/3/e75/>. Others include the joint program by a British
company specializing in AI DeepMind and the National Health Service (NHS) (“Working with the NHS)” DeepMind Website<https://deepmind.com/applied/deepmind-health/working-nhs/>; personalized medicine for cancer and pain management support
by FRONTEO Healthcare in Japan (AI technology for the ear where more advanced and better quality medicine is anticipated”)
FRONTEO Healthcare website <https://www.fronteo-healthcare.com/diagnoses/> (in Japanese).; the support software “MENTAT”
by Otsuka Digital Health (co-funded by Otsuka Pharmaceutical and Japan IBM) for building databases of various symptoms ofmental disorders that are difficult to quantify using electric chart data and for facilitating appropriate diagnosis using IBM’s AI“Watson” (“About MENTAT” Otsuka Digital Health website <https://www.mentat.jp/jp/service> (in Japanese).; and AI developed
by Hitachi for predicting the risk of rehospitalization of patients with heart diseases and presenting its grounds (“Hitachi and Partners
HealthCare succeeds in high-accuracy prediction of rehospitalization risks of patients with heart diseases using AI,” 2017.12.12.
Hitachi, Ltd. website <http://www.hitachi.co.jp/New/cnews/month/2017/12/1212.html> (in Japanese). Some improve thesatisfactory level of patients during hospitalization, instead of directly supporting diagnosis and treatment. For example, IBM usesa smart speaker using AI “Watson” to perform simple operations of equipment and to answer questions within hospital rooms.
“Thomas Jefferson University Hospitals Plans Cognitive Hospital Rooms powered by IBM Watson Internet of Things,” 2016.10.4.
IBM
Website<https://www-03.ibm.com/press/us/en/pressrelease/50692.wss>.
OthersincludeiCarbonX<https://www.icarbonx.com/>, Comarch <https://www.comarch.com/healthcare/>, and Cogito <http://www.cogitocorp.com/>.
42 「PROMPT（Project for Objective Measures Using Computational Psychiatry Technology）」Keio University Integrated Innovation
Lab for Psychiatry website <http://www.prompt-keio.jp/>; Taishiro Kishimoto, “Overview of the project “PROMPT”:
Characteristics of medical equipment (analysis sensor) and analysis methods, and realities of the use of AI,” Japan Human Sciences
Foundation, op.cit. (34), pp.41-47. The author (Fujita) is a member of the project.

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protection of personal information, accuracy of evaluations (including trust regarding the results of AI’s
judgments), clarification of responsibility in the event of an error, acceptance by the community of healthcare
professionals, approval as medical equipment, role of AI in the assessment of medical fees, and the treatment
of intellectual property rights regarding data. There are other issues to be considered in the future. Examples
include the influence on the relationship between doctors and patients, education for healthcare professionals
who use the equipment, ways to handle people who do not want to use the equipment (who do not want tobe analyzed), measures against economic and social discrimination associated with the results, and ways tohandle the situation when general people other than doctors and psychiatrists become able to use theequipment.43 These are issues commonly seen in other cases as well.
There are still other cases of the use of AI and robotics; for instance, chat bots (conversation programs)
and robots (including those that do not necessarily have conversation capabilities via language) based on text
information, such as academic papers and medical charts, are used to have conversations with patients, suchas patient interviews and health advice sessions. For example, the Jichi Medical University is working on
the research and development of AI for supporting doctors’ diagnosis called “White Jack,” which is capableof indicating the name of the diagnosis of highly suspected diseases based on information from patientinterviews and displaying information on recommended tests and prescriptions on an electronic chart.44
Further, various uses of robotics especially for dementia are note being discussed.45
Gathering of data for the learning and accuracy of diagnosis support creates problems when it mainly
uses text information. In addition, technically, the standardization and structure of data for learning are
considerable problems. 46 In terms of robotics, there are issues other than cost, such as psychologicalobstacles to the use of robotics and realistic effects of robotics,47 but robotics is much anticipated as a means
to contribute to reducing medical task burdens, including nursing care and elderly care.
(4) Surgery support
Because surgeons and emergency doctors are frequently required to make quick decisions during
surgeries, they bear a considerable burden both mentally and physically. Even if the number of doctors as awhole increased from 2004 to 2014, the number of surgeons decreased, and the number of young surgeons
43 Based on the findings of the investigations and studies by the author (Fujita).
44 Shizukiyo Ishikawa, “Development of a comprehensive medical assistance system centered around AI,” Jichi Medical University

Regional Medicine Open Lab News Letter, Vol.107, 2016.7. <http://www.jichi.ac.jp/openlab/newsletter/letter107.pdf> (inJapanese).. Other cases include the British company Babylon, which provides a chat bot for diagnosis and triage (determination of
the priority of treatment) <https://www.babylonhealth.com/>, the American company Catalia Health, which provides aninteractive-type health management robot “Mabu” <http://www.cataliahealth.com/>, the Japanese companies Ubie and NAM,
which provide a patient interview system using AI <http://www.company.dr-ubie.com/> (in Japanese) and <http://nam-inc.jp/>,and the Japanese company exMedio, which studies the use of AI for clinical advice for doctors (“Diagnosing skin diseases with AI
is becoming reality soon, image gathering is key,” Nikkei Sangyo Shimbun, 2017.6.30, p.9). (in Japanese).
45 ninninPROJECT website <http://ninnin-project.com/> (in Japanese); “What is Palro?” Daiwa House Website<http://www.daiwahouse.co.jp/robot/paro/products/about.html> (in Japanese); Palro Website <https://palro.jp/> (in Japanese). For
trends in service robots in the field of elderly care and medicine, see Ministry of Internal Affairs and Communications (ed.), White
Paper: Information and Communications 2016, pp.156-162.
<http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h28/pdf/28honpen.pdf> (in Japanese).
46 Kazuhiko Oe, “Possibilities of utilizing medical information and data obtained from wearable devices for preemptive (preventive)
medicine and drug discovery and future challenges,” Japan Human Sciences Foundation, op.cit. (34), pp.21-28.
47 Ministry of Internal Affairs and Communications. op.cit. (45), pp.161-162. Conversely, Hiroshi Ishiguro, professor of Osaka
University and others note that autistic children sometimes open their heart to robots and androids rather than to humans. Aya
Nakae et al., “Potentials of medical support by android,” Journal of orthopedics and traumatology, Vol.58 No.8, 2015.7, pp.10571061.

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below the age of 40 is continuously decreasing as well.48 It is an urgent issue to reduce the burden ofsurgeons, and expectations are placed on AI and robotics to solve the issue.
Similar to AED as equipment for supporting diagnosis and treatment, surgery support robots using AI
in a board sense, such as “da Vinci”49 for supporting endoscopic surgeries, have already been put to practicaluse. Projects have been launched to optimize surgeries using the Internet of Things (IoT) and robot
technology, such as “OR.NET”50 in Germany and “Medical Device “Plug-and-Play” (MD PnP)51 in the
US. In Japan, similar programs have been launched utilizing “OPeLiNK,” such as “Smart Cyber Operating
Theater (SCOT)” of Tokyo Women's Medical University. 52 These projects are seeking to establish anavigation system by connecting medical devices with each other that is capable of optimizing surgeries
using sensing technology and computing technology, storing surgery information in servers and analyzing it
as big data, and making optimal decisions in real time. As with the mechanization of labor in other fields, it
is hoped that efficiency, safety, and accuracy will increase, but there are limitations regarding the mechanicalperformance and cost-related issues. Liability for damages associated with malfunctions of robot surgery
systems is also discussed.53
Takanori Fujita, National Center for Global Health and Medicine

Ⅱ Elderly Care
1. Elderly care and caretakers
Japan has now become an aging society, and the number of users of care prevention services and nursing
care services increased from about 5.66 million people in 2013 to about 6.14 million people in 2016.54 Thereis an urgent need to secure care staff to address this situation, but it is estimated that there will be a demand-

Ministry of Health, Labour and Welfare, “2014 Survey of Physicians, Dentists and Pharmacists,” 2015.12.17, pp.36, 40.
<http://www.mhlw.go.jp/toukei/saikin/hw/ishi/14/dl/gaikyo.pdf> (in Japanese).
49 The da Vinci system was developed by the American company Intuitive Surgical and has been put to practical use following
approval by the FDA in 2000. As of September 2017, 4,271 units have been implemented worldwide. In Japan, the system was
granted pharmaceutical approval as medical equipment in 2009. As of the end of September 2016, 237 units have been implemented.
“About da Vinci” Intuitive Surgical Website https://www.intuitivesurgical.com/products/davinci_surgical_system/>; “FAQs,”
same as above <https://www.intuitivesurgical.com/products/products_faq.php>; “About da Vinci: Situations surrounding
pharmaceutical approval,” Japan Robotic Surgery Society <http://j-robo.or.jp/da-vinci/yakuji-shonin.html> (in Japanese).; “Aboutda Vinci: How they have been introduced,” same as above <http://j-robo.or.jp/da-vinci/nounyu.html> (in Japanese).
50 OR.NET Website <http://ornet.org/>.
51 MD PnP Website <http://www.mdpnp.org/>.
52 OPeLiNK is a system customized for medical purposes based on a communication system developed for integrating various robots
and operating them in factories. SCOT improves the accuracy and safety of surgeries by coordinating and connecting various
medical devices using OPeLiNK and integrating and grasping the progress of surgeries and patients’ situation. Japan Agency for
Medical Research and Development et al., “Completion of a prototype model of ‘Smart Cyber Operating Theater’ Accelerating
empirical development of coordination and connection of various medical devices using IoT and starting deliberation of the clinicaluse of treatment system,” 2016.6.16. <https://www.amed.go.jp/news/release_20160616.html>. (in Japanese).
53 Fumio Shimpo, “Legal issues and alteration of national consciousness toward social implementation of AI,” Nishimura (ed.). op.cit.
(18), pp.52-56.
54 Ministry of Health, Labour and Welfare, “2016 Survey of Long-term Care Benefit Expenditures: Summary Report,” 2017.8.31,
p.3. <http://www.mhlw.go.jp/toukei/saikin/hw/kaigo/kyufu/16/dl/11.pdf>. (in Japanese).
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supply gap of 377,000 people in 2025.55 The jobs-to-applicants ratio of care professions is constantly high,56
while it is difficult to secure employment mainly due to low wages, great physical and mental burdens, andlow social valuation. Some note dissatisfaction with working conditions, such as the shortage of manpowerand difficulty in taking paid vacation.57
As for relationships of caretakers with those requiring care, 58% are family members living together(spouse: 25.2%, son or daughter: 21.8%, spouse of son or daughter: 9.7%), 12.2% are family members living
separately, and 13.0% are service providers. Of the caretakers living together, 66.0% are female.58 The
number of those who left care professions (October 2011 to September 2012) was 101,100. Of them, 80.3%
were female.59
As discussed above, securing a care workforce is an urgent task, while there is the reality that the shareof caregiving by family members (mostly women) is very high. According to the “Survey of elderly people
bedridden at home” carried out by the Japan National Council of Social Welfare in 1968, the estimatednumber of elderly bedridden people was about 200,000 back then, and as for “main caretakers,” 25.1% werea “spouse (mainly wife)”, 49.8% were the “wife of the son,” and 14.5% were a “daughter.”60 As of 2013,
70% of caretakers were family members or relatives. As for the proportion of family member caretakers
living together, 28.6% were the “wife,” 19.1% were a “daughter,” 17.8% were the “wife of the son,” 3.3%
were a “female relative,” 13.9% were the “husband,” 16.3% were a “son,” 0.4% were the “husband of thedaughter, and 0.6% were a “male relative.”61 These results show that care has been given mainly by family
members, and this has not changed, but the proportion of caretakers who are the “wife of the son” hassignificantly dropped over 45 years, while the proportion of daughters and sons as caretakers has increased.
Support for these caretakers will be discussed in the next section.

Ministry of Health, Labour and Welfare, “Estimation of the Demand/Supply of Care Workforce (final figure) toward 2025,”
2015.6.24, p.1.
<http://www.mhlw.go.jp/file/04-Houdouhappyou-12004000-Shakaiengokyoku-ShakaiFukushikibanka/270624houdou.pdf_2.pdf>. (in Japanese).
56 In 2015, the ratio of all professions was 1.08, whereas the ratio in the domain of elderly care was 2.59. Welfare Manpower
Promotion Office, Welfare Promotion Division, Social Welfare and War Victims' Relief Bureau, Ministry of Health, Labour andWelfare, “About efforts for securing welfare and care workforces,” 2017.6.7, p.3. (regional strategy session for securing care
workers
(5th)
(June
7,
2017)
Material
1)<http://www.mhlw.go.jp/file/05-Shingikai-12201000Shakaiengokyokushougaihokenfukushibu-Kikakuka/0000167734.pdf>. (in Japanese).
57 Industrial Structure Policy Division, Economic and Industrial Policy Bureau, Ministry of Economy, Trade and Industry, “Reportby the Study Group on the Provision of Long Term Care Services in Response to Future Long Term Care Demand,” 2016.3.24,
pp.31-32. <http://www.meti.go.jp/press/2015/03/20160324004/20160324004-1.pdf>. (in Japanese).
58 Ministry of Health, Labour and Welfare, “Summary of 2016 National Livelihood Survey,” 2017.6.27, p.30.
<http://www.mhlw.go.jp/toukei/saikin/hw/k-tyosa/k-tyosa16/dl/16.pdf>. (in Japanese).
59 Bureau of Statistics, Ministry of Internal Affairs and Communication, “Summary of the Results of 2012 Employment Status
Survey,” 2013.7.12, pp.72-73. <http://www.stat.go.jp/data/shugyou/2012/pdf/kgaiyou.pdf>. (in Japanese). Because those aged 60
or older account for about 50% of caretakers, the issue of the elderly taking care of the elderly must also be discussed (same, p.70).
60 Chiiko Ishiguro, “About the implementation of the survey of ‘bedridden elderly people’,” Japanese journal for public health nurse,
Vol.24 No.8, 1968.8, pp.26-29. (in Japanese).
61 Akiko Saito, “Family and elderly care,” Natsuki Nagata & Hiroto Matsuki (eds.), An introduction to Sociology of Family,
Shinsensha, 2017, pp.65-81. These figures were calculated based on the elderly care sheet of the National Livelihood Survey. (inJapanese).
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2. Development of robotic devices for elderly care and institutions for facilitating their introduction
The “Growth Strategy 2017”62 and “Japan's Robot Strategy”63 set a goal of making the size of thedomestic market for robotic devices for elderly care worth 50 billion yen by 2020. The Ministry of Economy,
Trade and Industry and the Ministry of Health, Labour and Welfare are playing a central role in supporting
caretakers through the development of robotic devices for elderly care.64 Their efforts include reducing care
professionals’ physical burden, decreasing the turnover rate of care professions and enriching care services
through streamlining tasks, and creating a regional comprehensive care system (discussed later) throughcontinuous support for the independent livelihood of the elderly in the community.
(1) Support for care and welfare facilities and care service providers (experiment and introduction projects)
Below are representative subsidized projects for supporting care and welfare facilities and care service
providers.
The “Special project for supporting introduction of elderly-care robots, etc.” was a one-time-only
project realized by the 2015 supplementary budget, and it subsidized a maximum of 927,000 yen per facility
and provider as expenses for introducing elderly care robots priced over 200,000 yen. The project is finishedat this point.
The “Project for supporting introduction of nursing-care robots and empirically studying its
effectiveness” (2017) was a one-time-only project realized by the second supplementary budget of 2016 to
subsidize the cost for introducing elderly care robots (maximum 2,000,000 yen) and cost for investigating(fixed amount of 300,000 yen) facilities that would cooperate in an empirical study of elderly care robots.
Many of the “projects for supporting introduction of elderly-care robots” implemented by prefecturesusing the “Fund for Comprehensively Securing Regional Health and Long-term Care in Japan (Fund forlong-term care)”65 subsidize half the cost of introducing elderly care robots (maximum 100,000 yen per
robot).66 Some municipalities set their own subsidy rate, such as Komatsu City in Ishikawa Prefecture (upto 500,000 yen per service provider, subsidizing no higher than 1/2 for units purchased and no higher than
2/3 for units leased).67
The Ministry of Health, Labour and Welfare considers that elderly watching robots (watching sensors)
can replace a portion of the staff working in special nursing homes and announced that conditions for adding“Growth Strategy 2017” op.cit. (7).< https://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/miraitousi2017_inttv_prgrm.pdf >“Japan’s Robot Strategy” (decided by the Headquarters for Japan's Economic Revitalization on February 10, 2015), Office ofPrime Minister Website <https://www.kantei.go.jp/jp/singi/keizaisaisei/pdf/miraitousi2017_inttv_prgrm.pdf>.
64 Division of Support for the Elderly, Health and Welfare Bureau for the Elderly, Ministry of Health, Labour and Welfare &Industrial Machinery Division, Manufacturing Industries Bureau, Ministry of Economy, Trade and Industry, “Priority areas towhich robot technology is to be introduced in nursing care of elderly” (formulated in November 2012 and revised in October
2017) <http://www.mhlw.go.jp/file/04-Houdouhappyou-12304250-Roukenkyoku-Koureishashienka/0000180157.pdf>. (inJapanese).
65 This is a fund established at prefectures using the increase in revenue from consumption tax to improve care facilities and securecare professionals.
66 Division of Support for the Elderly & Division of Promotion, Health and Welfare Bureau for the Elderly, Ministry of Health,Labour and Welfare, “About the projects using the “Fund for Comprehensively Securing Regional Health and Long-term Care in
Japan” (Fund for long-term care)” (notification to the long-term care insurance department of each prefecture), 2015.1.16;“Subsidies for 2017 projects for supporting introduction of elderly-care robots [list by prefecture],” updated on October 17, 2017.
Kaigo Robot ONLINE Website <https://kaigorobot-online.com/contents/33>. (in Japanese).
67 “Subsidies for projects to promote introduction of elderly-care robots,” Komatsu City Website <https://kaigorobotonline.com/contents/33>. (in Japanese).
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nursing care fees, which are added according to the number of personnel allocated during nighttime, will be
relaxed for care service providers introducing sensors in the 2018 revision of nursing care fees.68(2) Support for those in need of long-term care (long-care insurance program)Some elderly care robotic devices are covered by the insurance payment for the lending of welfare
equipment appropriate for the level of care needed, as defined by the long-term care insurance program.69
The number of welfare devices leased using the long-term care insurance system is constantly increasing,although there are some variations in the degree of the increase depending on the type of device.70 As for
elderly care robotic devices not covered by insurance payments from the long-term care insurance system at
the moment, possibilities for including them as devices covered for lease or purchase may be discussed atthe “Committee for evaluating welfare devices and home renovation covered by the long-term care insurance
system” of the Ministry of Health, Labour and Welfare.71 As of 2014, the committee decided to look into
the possibilities of including “communication robots,” “elderly watching robots,” “robotic suits,” and elderly
care robots for “toilet support” and “mobility support” within the coverage of the long-term care insurance
system.72 In addition to the above, the committee is also looking into the possibility of including “drugadministration robots” and “sensor devices for detecting wandering dementia patients” during the meetings
held in 2015 and onward.73 Some municipalities have carried out projects for leasing elderly care robotic
devices not covered by the long-term care insurance system, for which users will bear 10% of the cost.74
3. Long-term care support using elderly care robotic devices
Hereinafter, we will discuss “communication robots” and “elderly watching,” as particularly relevant
to AI among the elderly care robotic devices designed for supporting people in need of long-term care.
(1) Communication robots
In 2016, the AMED’s “Project to Promote the Development and Introduction of Robotic Devices for
Nursing Care” implemented a large-scale empirical experiment on the use of communication robots in thefield of long-term care. The report containing the results of this experiment showed that about one-third of“About revisions in each service in the 2018 revision of nursing care fees” (Committee for Long-Term Care Benefit Expense of
the Social Security Council (158th meeting) (January 26, 2018) (Reference material 1), pp.116, 211.
<http://www.mhlw.go.jp/file/05-Shingikai-12601000-SeisakutoukatsukanSanjikanshitsu_Shakaihoshoutantou/0000192302.pdf>. (in Japanese).
69 For example, a walking support device equipped with robot technology developed by RT.WORKS called “Robot Assist Walker
RT.2,” “Robot Assist Walker RT.2” RT.WORK Website <https://www.rtworks.co.jp/eng/product/rt2.html>.
70 Ministry of Health, Labour and Welfare, “Summary of the Survey of Long-term Care Benefit Expenditures,” 2017.8.31, p.11.
<http://www.mhlw.go.jp/toukei/saikin/hw/kaigo/kyufu/16/dl/11.pdf>. (in Japanese).
71 Ministry of Health, Labour and Welfare, “Guidelines for developing welfare devices and elderly-care robots.” 2014.3, p.10.
Association for Technical Aids Website <http://www.techno-aids.or.jp/research/robotebiki_mhlw_140922.pdf>. (in Japanese).
72 “About welfare devices to be used in the priority areas to which robot technology is to be introduced in nursing care of elderly,”
2014.10.28. (Committee for evaluating welfare devices and home renovation covered by the long-term care insurance system (1stmeeting in 2014) (October 28, 2014) Material 5-3), Ministry of Health, Labour and Welfare Website<http://www.mhlw.go.jp/file/05-Shingikai-12301000-Roukenkyoku-Soumuka/0000094783.pdf>. (in Japanese).
73 “About the types of welfare devices to be considered,” 2015.11.9. (Committee for evaluating welfare devices and homerenovation covered by the long-term care insurance system (1st meeting in 2015) (November 9, 2015). Same as above<http://www.mhlw.go.jp/file/05-Shingikai-12301000-Roukenkyoku-Soumuka/1109-5-1.pdf>. (in Japanese).
74 “About the model project for elderly-care devices leasing,” Okayama City website<http://www.city.okayama.jp/hofuku/hokenfukushiseisaku/hokenfukushiseisaku_00084.html>. (in Japanese).
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the 866 subjects of the experiment experienced improvement in the areas of “self-care” and “motor &mobility,” which are closely linked to long-term care in particular.75 This report divides communication
robots into three categories, “situation detection and adaption robots,” “environment & operation reactive
robots,” and “program implementation robots replacing caretakers.”76 “Situation detection and adaption
robots” refer to robots that detect the status (hours in the recumbent position, hours in the sitting position,
hours spent in the room) of the person to be cared for and that take actions with purposes. For example, “AIsense” by IntelliVoice responds to instructions from the elderly watching robot and takes actions such asencouraging a person who has spent more than a certain number of hours in the sitting position to do some
exercise.77“Environment & operation reactive robots” refer to robots that react to operations given to the robot(including touching and talking) and the surrounding environment. “Program implementation robots
replacing caretakers” refer to robots that replace caretakers in terms of encouragement (mainly recreation,etc.). Many of the functions categorized or “environment & operation reactive robots” and “program
implementation robots replacing caretakers” have already been put to practical use. For example, acommunication robot developed by Vstone, robots equipped with communication functions using an AI
technology brand of NTT Group “corevo,”78 and “PALRO”79 developed by Fujisoft are known amongthem.
In terms of PARLO, units for professional use and for research purposes are already available in themarket, and units for home use are already in the process of commercialization. In addition to the current
communication functions and remote-call functions, a function that sends information, such as the user’s
conversations, to family members living separately will be given to units for home use. 80 Equipping
communication robots with the elderly watching technology is very novel because the robots will be able toperform “communication” and “watching over” functions simultaneously.
(2) Elderly watching support
The conventional elderly watching support technology was designed to detect motions when patients
get up from the bed, turn over, fall, and wander based on changes in the signals, such as weight, motion,body temperature, and sound volume, using sensing technology. In recent years, development has been under
way to have AI acknowledge the information and accumulate it as data in the network so that the system canplan preventive measures, not only by watching the motions mentioned above, but also by detecting
Yayoi Okawa, “Report of the large-scale empirical experiment on the use of communication robots in the field of long-term care:
Project to Promote the Development and Introduction of Robotic Devices for Nursing Care (project for standard setting andevaluation) ‘Survey of the development of robotic devices for nursing elderly care’,” 2017.5.31 (revised on 2017.7.27), pp.12-13.
“Robotic Devices for Nursing Care Project” Website <http://robotcare.jp/wpcontent/uploads/2017/07/communi_robo_veri_test_report.pdf>. (in Japanese).
76 Same as above, pp.4-7.
77 “2016 Project to Promote the Development and Introduction of Robotic Devices for Nursing Care (project for standard setting
and evaluation) “Survey of the development of robotic devices for elderly care,” Robot list: A-Isense” Japan Agency for Medical
Research and Development Website <https://www.amed.go.jp/content/000003893.pdf>. (in Japanese).
78 “Revitalizing the robot market with cloud service capable of handling various robots and purposes: Cloud-based robot platform
service ‘RoboConnect’ launched on September 1,” 2016.8.30. NTT Group Website <http://www.ntt.co.jp/corevo/topics11.html>.
(in Japanese).
79 “PARLO” Fujisoft Website < https://palro.jp/en>.
80 “PARLO launches the first home use version capable of sending information of users to family, Fujisoft,” 2017.10.12. Nursing
care news site “Joint” Website <http://www.joint-kaigo.com/article-5/pg31.html>.(in Japanese).
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predictive behaviors and understanding action patterns. Recent ones include OWLSIGHT 81 by Care
Dynamics.
4. Task support using AI(1) Automatic creation of care plans
A care plan refers to a plan for using long-term care services that a care manager (long-term care support
specialist) creates based on the health condition of the person receiving care, activities of daily living, theperson’s wishes, and the living situations of family members, including short-term and long-term goals.
There is a great burden associated with the creation of care plans,82 and AI technology is expected to reducethat burden.
IT tools for automatically creating care plans are covered by the Ministry of Economy, Trade andIndustry’s “Project to support IT introduction to enhance services and productivity (IT Introduction
Subsidies),83 and they are expected to reduce the burdens associated with long-term care.
(2) Studies regarding the automatic creation of care plans
The Saint-Care Holding Corporation, which provides care services, carried out joint research for
applying AI in the creation of care plans together with Stanford Artificial Intelligence Laboratory andStanford Clinical Excellence Research Center and published a report in March 2017. During this research,
they used data on users of a long-term care insurance system living in Wako City, Saitama Prefecture (2010
to 2015) (8,595 people) to have AI learn items for the certification of long-term care needs, reports from thedoctors in charge, and the weekly schedule for service usage as datasets, to read the actual cases, and to
output the frequency of the use of services. The results of evaluating the care plans based on the output
revealed some issues, such as the need to have it learn more data, including information from users of thelong-term care insurance system (current medical history, economic status, living situation, etc.), plans forlong-term care prevention services and support, prognostic prediction, and service contents, as well as theneed to clarify the definitions of independence or independent support. 84 In April 2017, Saint-Careestablished a new company called “Care Design Institute Inc.” to achieve AI utilization in elderly caresettings. The company also launched an empirical project for care plan preparation using AI together with
Toyohashi City, Aichi Prefecture, in July of the same year.85
81“OWLSIGHT,” Care Dynamics Website <http://www.care-dynamics.jp/owlsight/>. (in Japanese).

82 For example, according to the survey conducted by the Nagasaki Care Manager Liaison Committee, it took 75 minutes to create aplan for continued users and 120 minutes to create a plan for new users as of 2012. “Proposal to the national government: Timerequired for care management, Nagasaki Care Manager Liaison Committee,” 2012.1.4. Silver Industry News Website<http://www.care-news.jp/news/caremana/care_nagasaki_11_12_10.html>. (in Japanese).
83 This is a project for subsidizing a portion of the expenses, such as operating costs, for the introduction of IT tools (software, service,etc.) to increase the productivity of small and medium-sized businesses and small-sized service providers. “IT Introduction
Subsidies (2016 supplementary budget, project to support IT introduction to enhance service and productivity),” Service Design
Engineering Council
Website <https://www.it-hojo.jp> (in Japanese). This is a project realized by the 2016 supplementary budget, but a similar project will
be implemented using the 2017 supplementary budget.
84 Saint-Care Holding Corporation, “Report on the research of possibilities of AI introduction in care plan preparation and its
challenges for promoting independence support,” 2017.3, pp.3-4.
<http://v4.eir-parts.net/v4Contents/View.aspx?template=ir_material&sid=71259&code=2374>. (in Japanese).
85 Yoshikatsu Masuda, “Empirical project for care plan preparation by AI launched,” 2017.7.12. Nikkei Digital Health Website<http://techon.nikkeibp.co.jp/atcl/news/16/071108329/?ST=health>. (in Japanese).

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5. Problems surrounding elderly care
The introduction of robotic devices for elderly care may mitigate the physical and mental burdens ofcaretakers and people in need of care. On the other hand, there are problems that cannot be solved easily at
the moment. Below, we will describe two of these problems.
(1) Watching over or monitoring?
Monitoring people in need of care using sensing technology has benefits, such as predicting andpreventing dangerous behaviors, whereas some note the problem of interference with the free will of people
in need of care.86 Robots for supporting both communication and elderly watching, such as PARLO forhome use, come with the issue of protecting the right to privacy of people in need of care.
There is a report that when developing and introducing a camera system for supporting elderly watchingfor use in long-term care facilities, the facility selected for the project expressed reservations about thedevelopment and introduction of the system at first, because one of the prefecture’s nursing care facility
evaluation items stated that “the elderly should not be monitored with cameras in an effort to facilitate elderly
watching.”87 As indicated by this case, it is difficult to determine where to draw a line between elderly
watching using a watching support system and monitoring. Similar problems are expected to occur whendeveloping and introducing elderly watching support technology in the future.
(2) 2017 amendment to the Long-Term Care Insurance Act centered on the integrated community caresystemThe amendment to the Long-Term Care Insurance Act in 201788 reinforced a system for providing
home, healthcare, nursing care, prevention, and livelihood support comprehensively, called the “theintegrated community care system.” What is noteworthy here is the introduction of coexistence-type services.
Traditionally, when people with disabilities reach the age categorized as “elderly,” they have no choice but
to stop using welfare service for persons with disabilities, which they had been using, and to start using
service providers covered by long-term care insurance, owing to the principle that prioritizes long-term careinsurance. This amendment eliminated the distinction between the elderly and the disabled; thus, people withdisabilities who have turned 65 are not allowed to continue living in a facility they are used to. Along with
the introduction of coexistence-type services, appropriate utilization of the limited human resources engaging
in welfare according to the reality of communities was addressed. However, because the special knowledge
and skills required in welfare for the disabled and long-term care for the elderly are different, there is anotherissue of securing care professionals who are specialized in both.
As the issue of the expertise of care professions in the coexistence-type services has been noted, it isnecessary to discuss how robotic devices for elderly care and AI can substitute or support the tasks of care
professionals.
Noel Sharkey and Amanda Sharkey, “The eldercare factory,” Gerontology, Vol.58 No.3, 2012.4, pp.282-288.
Taro Sugihara et al., “Problems associated with the development and introduction of a camera system for supporting demented
elderly watching at group homes,” Journal of Sociotechnology Research Network, Vol.7, 2010.3, pp.54-65.
<https://www.jstage.jst.go.jp/article/sociotechnica/7/0/7_0_54/_pdf/-char/ja>. (in Japanese).
88 Act for the Partial Revision of the Long-Term Care Insurance Act, etc. for the Reinforcement of the Integrated Community Care
System (Act No. 52 of 2017). It came into effect on April 1, 2018 (Supplementary Provision Article 1).
86
87

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Naonori Akiya, Yamaguchi University

Ⅲ Art and Design
1. Work of people involved in art and AI technology
Some note that it is difficult for AI to replace jobs that require creativity in the field of art and design.89
On the other hand, AI that can automatically perform part of production activities is now being developed
and used, such as composition, musical performance, coloring of line drawings, and website design. The
products created by AI may be accepted as works of art and distributed in the market,90 or some expect that
AI may bring out new aesthetic value that differs from that of works of art created by people.91 In this chapter,
we will overview the ongoing research projects and service trends in this area and then discuss AI with afocus on AI’s influence on creators involved in the process of developing ideas and producing works.
Creators’ work styles are diverse, from freelancers (self-employed), those who have an employment
contract, to those engage in subcontract work. Many of them are faced with low incomes, long working
hours, and a lack of social security, struggling to balance work with life.92 On the other hand, for clients who
use the works of creators for advertising purposes, the primary objective is to transmit information they wantto convey effectively. For example, in the field of composition, it is said that anyone will be able to generatemany pieces of music suited for this objective using AI technology.93 If clients can get pieces of music theyneed in a short amount of time at lower costs through composition services using AI, this could be an
attractive application. Similar advantages may apply in fields other than composition, as discussed later.
2. Artwork creation services using AI and the status of research and development(1) Services to create music, images, and the like
Since the mid-2010s, there have been services in Japan and overseas that provide data generated throughsystems using machine learning and deep learning, which do not require specialized knowledge on the partof users. Examples include music creation (composition), 94 coloring of line drawings and image

89 Carl Benedikt Frey and Michael Osborne, The Future of Employment. How susceptible are jobs to computerisation?, Oxford

Martin Programme on Technology and Employment, 2013, p.26.
<https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf>.
90 Sarah Cascone, “Google’s ‘Inceptionism’ Art Sells Big at San Francisco Auction,” 2016.3.2. artnet news
<https://news.artnet.com/market/google-inceptionism-art-sells-big-439352>.
91 Hideki Nakazawa, “Manifesto of Artificial Intelligence Art and Aesthetics,” 2016.4.25. Artificial Intelligence Art and Aesthetics
Research Group website https://www.aloalo.co.jp/ai/manifesto_e.html >92 Yayoi Yosizawa, “Labor in art projects,” 2015.6.9. Explat Website <http://www.explat.org/news/2015/Yoshizawa.html> (inJapanese).
93 Jordan Passman, “Music As A Commodity: Songwriting With Artificial Intelligence,” Mar 3, 2017. Forbes Website<https://www.forbes.com/sites/jordanpassman/2017/03/03/music-as-a-commodity-songwriting-with-artificial-intelligence/>.
94 “Amper Music” in the US and “Jukedeck” in the UK are representative music creation services. Music will be automaticallycreated once the genre, mood, and time of performance are selected. Amper Music Website <https://www.ampermusic.com/>;
Jukedeck Website <https://www.jukedeck.com/>.

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processing,95 and website design.96 By using these services, users can freely create and transmit music data
or image data with a certain level of quality with easy-to-use manipulations without worrying about feesfrom using copyrights.
(2) Research and development projects
Research and development for creating works according to the taste and sense of individuals is also
under way. In the domain of music, the research group led by Professor Noriko Otani of Tokyo City
University is developing a system for composing music consistent with the taste of individuals based on theirchoices of music, 97 while the Center of Innovation (COI) Site (“Developing 'Super Nippon-Jin' by
Activating Human Power”) led by Osaka University is developing a system for measuring the brain waves
of individuals and composing music suited to their mood.98 “Flow Machines”99 is a project that Sony
Computer Science Laboratories is working on for extracting a composer’s style from data, such as existingmusical scores and lyrics, and creating new music based on them.
In the field of fine arts, there is a project called “The Next Rembrandt,”100 which the Delft Universityof Technology and Mauritshuis jointly works on as a project for generating images that imitate the styles ofexisting artists. In 2016, the project announced a work of art created by extracting the painting style ofRembrandt van Rijn101 from the scanned data of his works, generating a new Rembrandt-style portrait, andoutputting it as an oil painting-like image using a 3D printer.
(3) Value of the creations of AI
A piece created using “Deep Dream,” an image recognition and generation system developed by
Google, was purchased for 8,000 dollars through an auction in the United States.102 This indicates that AIcreated works of art have market value. At the moment, however, some say that these services and researchand development projects are not at a level that can replace human composers or artists.103 Right now, it isnoted that what are considered AI creations are not necessarily what AI created autonomously; rather, they
95 PaintsChainer is one of the coloring services of line drawings for illustrators. As cases of image processing, there are apps such as

Prisma and Pikazo for converting photos into images that imitate the styles of impressionists or cubism. PaintsChainer Website<https://paintschainer.preferred.tech/index_ja.html>(in Japanese); “Prisma.” Google Play Website<https://play.google.com/store/apps/details?id=com.neuralprisma&hl=ja>; “Pikazo – AI Art.” iTunes Website<https://itunes.apple.com/us/app/pikazo-ai-art/id1100723148?mt=8>.
96 For example, there is an AI-based website production service in Japan called HONMONO, as well as The Grid and Wix
overseas.
HONMONO Website <http://honmono.click/> (in Japanese); The Grid Website <https://thegrid.io/>; “The Future of Website
Building Is Here,” WiX.com Website <https://www.wix.com/about/adi-get-access>.
97 “Professor Otani studies automated creation of music using artificial intelligence,” 2016.11.8. Mainichi Shimbun Website<https://mainichi.jp/univ/articles/20161108/org/00m/100/015000c>(in Japanese).
98 Osaka University, “Developing artificial intelligence for automatically composing music based on the brain waves,” 2017.1.16.
Osaka University Resou Website <http://resou.osaka-u.ac.jp/ja/research/2017/20170116_1> (in Japanese).
99 Flow Machines Website <http://www.flow-machines.com>. Bach-style music called “DeepBach” and the Beatles-style music“Daddy's Car” created in this project have been released on YouTube.
100 The Next Rembrandt Website <https://www.nextrembrandt.com/>.
101 A Dutch painter who represents the baroque period. Famous for pieces such as The Night Watch (1642).
102 Alex Rayner, “Can Google's Deep Dream become an art machine?” Guardian, 2016.3.28.
<https://www.theguardian.com/artanddesign/2016/mar/28/google-deep-dream-art>.
103 For example, there was a reaction such as the following: “At the moment, I cannot find anything that has realized the quality ofmusic available in the market through fully automated composition.” (Hiromichi Yamada “AI composition grows rapidly, futureof music turning into commodity,” 2017.5.2. EYS Music School Website <http://www.eysmusicschool.com/media/ai_musiccomposition/> (in Japanese)).

66

are what has been created using AI as a tool with certain contributions from people.104 For example, theBeatles-style music released as a creation of “Flow Machines” was arranged by a human composer.105
Thus, it is probably still too early to conclude that the creations of AI have the same artistic value as
works created by people. Yet, if AI technology advances further in the future and if the quality of the works
created automatically exceeds that of creators, then we will no longer need to have humans produce suchworks. In the long run, to secure creators’ activities, we need to find some sort of added value that cannot becreated by AI, such as unique personalities and styles.
3. The issue of copyrights
The issue of copyrights is considered to be among influences of works created by AI have on creators.
In Japan, this issue has been vigorously debated among, for example, the “New Information Goods
Committee” of the Intellectual Property Strategy Headquarters.106
The Copyright Act in Japan (Act No. 48 of 1970) stipulates that a “work means a production in whichthoughts or sentiments are expressed in a creative way and which falls within the literary, scientific, artistic
or musical domain.” AI does not have such thoughts or sentiments, and the creations of AI are thus notconsidered to have copyrights.107 Then, do all of AI creations not have copyrights? The “Reports of works
produced with the aid of a computer” published in 1993 by the Copyright Council stipulated a policy thatworks autonomously created by systems do not have copyrights, but when systems are used by humans as atool, a copyright is granted to the person who operates the systems.108
If we look at cases overseas, the copyright of a British music creation service using AI called “Jukedeck”
belongs to “Jukedeck,” but users can use what AI has created for free. However, they can also purchase thecopyright from “Jukedeck.”109 The works painted with an AI-based painting program called “AARON”
created by Harold Cohen are exhibited at the Victoria and Albert Museum in the UK. On the website of themuseum, Cohen is listed as the artist who has the copyright, and AARON is listed as the painting tool and104Intellectual Property Strategy Headquarters, Verification, Evaluation, and Panning Committee, New Information Goods
Committee, “Report of the New Information Goods Committee: Toward establishment of intellectual property systemfoundational to the reinforcement of industrial competitiveness through promotion of the use of data and artificial intelligence(AI),” 2017.3, pp.25-26. Office of Prime Minister Website<http://www.kantei.go.jp/jp/singi/titeki2/tyousakai/kensho_hyoka_kikaku/2017/johozai/houkokusho.pdf> (in Japanese).
105 The commentary attached to the released movie for “Daddy's Car” says that the French composer Benoît Carré wrote lyrics forthe music generated by “Flow Machines” and arranged the song. “Daddy's Car: a song composed by Artificial Intelligence - inthe style of the Beatles.” YouTube Website <https://www.youtube.com/watch?v=LSHZ_b05W7o>.
106 Intellectual Property Strategy Headquarters, Verification, Evaluation, and Panning Committee, New Information Goods
Committee, op.cit. (104) (in Japanese).
107 Takushi Ootani, “Artificial intelligence, art, and copyright: If art isn’t an expression of thoughts and emotions, then why can wecall it the product of human creativity?” Journal of information processing and management, Vol.60 No.8, 2017.11, p.594.
<https://www.jstage.jst.go.jp/article/johokanri/60/8/60_594/_pdf/-char/ja>(in Japanese).
108 Agency for Cultural Affairs, “Report of the 9th subcommittee of the Copyright Council (related to computer-generated works),”
1993.11. Copyright Research and Information Center Website <http://www.cric.or.jp/db/report/h5_11_2/h5_11_2_main.html>(inJapanese). The same was confirmed in the report put together by the Intellectual Property Strategy Headquarters in 2016.
Intellectual Property Strategy Headquarters, Verification, Evaluation, and Panning Committee, Next Generation Intellectual
Property System Review Committee, “Report of the Next-generation Intellectual Property System Review Committee: Toward
establishment of next generation intellectual property system corresponding to digital networks,” 2016.4, p.22.
<https://www.kantei.go.jp/jp/singi/titeki2/tyousakai/kensho_hyoka_kikaku/2016/jisedai_tizai/hokokusho.pdf>(in Japanese).
109 Licensing term 3.3.7 of “Jukedeck” says that “Jukedeck” is and remains the owner of the copyright of the music generated, andlicensing term 3.4.1 says that if a user purchases a track, then “Jukedeck” transfers ownership of the track to the user.
“Licensing.” Jukedeck Website <https://www.jukedeck.com/licensing>.

67technique used.110 The museum sees the person who developed and used the system as the creator of thepaintings.
The Copyright, Designs and Patents Act 1988 (1988 c. 48) in the UK stipulates that, in the case of aliterary, dramatic, musical, or artistic work that is computer generated, the author shall be taken to be theperson by whom the arrangements necessary for the creation of the work are undertaken, in circumstancessuch that there is no human author of the work, acknowledging copyrights to computer-generated works(Section 9, (3) and Section 178).111 However, the arrangements necessary for the creation of a work are notdefined clearly. Therefore, we cannot deny the possibility that the verdict may differ depending on the degreeof AI’s involvement, and therefore, we need to be careful as to who holds the copyright.112
In addition, a problem arises for creators when the works to which they hold the copyright are used asdata for AI learning. In Japan, the “Interim Report of the Legislative and Basic Issues Subcommittee of the
Subdivision on Copyright, Council for Cultural Affairs” presented a view in February 2017 that using theworks of others as data for AI learning does not necessarily harm the interests of the persons who own the
copyrights, and their rights can be restricted for this purpose.113 The report notes the necessity to build
archives of data used for learning and to make it available to the public in relation to this. On the other hand,
it is difficult to make data open using uniform rules, because some businesses provide certain data and charge
related fees. It has been noted that it is necessary to establish a carefully crafted system, by looking into what
data should be open and what data should not be open by domain.114
4. Human creators of the future and their involvement with AI
There are at least the following three ways in which creators may be involved with AI technology: (1)playing a part in the development of AI that can be applied in the domain of art, (2) using AI technology asa tool for producing their own works, and (3) competing with AI technology when receiving production jobs.
Photos and paintings previously used to influence the styles of each works, and portrait painters faded
away. Similarly, it is expected that AI-generated works in the domain of art and creators’ artworks will
influence each other, which may change means of expression and job styles. Creators will probably be further
required to offer “something” that cannot be achieved by AI.
Reina Saijo, Kyoto University

110“0305-03.” V&A Search the Collections Website <http://collections.vam.ac.uk/item/O114802/0305-03-print-cohen-harold/>.

Japanese translation of the act: Yukifusa Oyama & Tetsuya Imamura (tra.) “Collection of Foreign Copyright Laws (53): United
Kingdom,” Copyright Research and Information Center, 2016.3. <http://www.cric.or.jp/db/world/england/england2.pdf>. The
right does not apply in relation to works created by computers (Article 79, (2) of the same act), and the copyright expires in 50
years, which is shorter than the 70 years set for ordinary works (Article 12, (7) of the same act). Works created by humans are thepremise of copyright protection in the copyright laws of other countries, including those in the US. Generally, works created by
AI autonomously (with no human creators) are not covered by copyright protection. Andres Guadamuz, “Artificial intelligenceand copyright,” WIPO Magazine, 2017, No.5, 2017.10, pp.14-19.
<http://www.wipo.int/export/sites/www/wipo_magazine/en/pdf/2017/wipo_pub_121_2017_05.pdf>.
112 Toby Bond, “Artificial Intelligence and IP – Part 2: IP in AI Generated Content,” 2017.6.16. DigitalBusiness.Law Website<http://digitalbusiness.law/2017/06/artificial-intelligence-and-ip-part-2-ip-in-ai-generated-content/>.
113 Legislative and Basic Issues Subcommittee of the Subdivision on Copyright, Council for Cultural Affairs, “Interim Report of theLegislative and Basic Issues Subcommittee of the Subdivision on Copyright, Council for Cultural Affairs,” 2017.2, pp.46-47.
.Agency for Cultural Affairs Website<http://www.bunka.go.jp/seisaku/bunkashingikai/chosakuken/pdf/h2902_chukanmatome.pdf>(in Japanese)..
114 Intellectual Property Strategy Headquarters, Verification, Evaluation, and Panning Committee, New Information Goods
Committee, op.cit. (104), pp.27-29 (in Japanese).
111

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Ⅳ Education
1. Overview of the domain of education
In the current Second Basic Plan for the Promotion of Education, four basic policy directions of theeducational administration are stipulated: (1) developing social competencies for survival, (2) developing
human resources for a brighter future, (3) building safety nets for learning, and (4) building bonds andestablishing vibrant communities. The plan also addresses problems, such as improving the situation ofbullying, non-attendance at school, and high school dropouts and securing opportunities for educational
advancement irrespective of students’ financial situation. 115 In addition, the following issues will be
addressed in the Third Basic Plan for the Promotion of Education to commence in fiscal year 2018; (1)changes in school attendance and work structures due to the declining birthrate and aging population, (2)changes in the industrial structure and society due to technological innovations and globalization, (3) fixationof disparities, such as poverty of children, (4) regional problems, such as regional gaps, and (5) changes inthe situations surrounding children.116
To solve these problems, it is necessary to take a number of measures, such as reforms to conventional
education and diversification of educational content, which would significantly increase the burden onteachers in schools. Therefore, the Ministry of Education, Culture, Sports, Science and Technology - Japanis working on a plan to establish ICT facilities in schools117 in order to streamline tasks and address abilities
to utilize information (including programming education) in the next Course of Study for Elementary School
to improve the ICT literacy among Japanese people.118 Thus, the ministry is gradually introducing ICT bothin educational environments and in educational content. In this paper, therefore, we will put a spotlight on
the use of AI and ICT mainly in elementary and secondary education.
2. Issues in the domain of education(1) Dealing with various learners
The proportion of non-attendance in elementary and lower secondary schools in 2016 was 1.35%; it
has been increasing since 2012 (1.09%).119 In addition to non-attendance, when children have difficulties
commuting to school due to sickness or financial reasons or when children and students come back to Japan“Basic Plan for the Promotion of Education” (Cabinet decision on June 14, 2013” Ministry of Education, Culture, Sports, Scienceand Technology Website< http://www.mext.go.jp/en/policy/education/lawandplan/title01/detail01/sdetail01/1373808.htm>.
116 Central Council for Education’s Committee for Basic Plan for the Promotion of Education, “Basic ideas toward development of
the Third Basic Plan for the Promotion of Education,” 2017.1.19, pp.3-8. Ministry of Education, Culture, Sports, Science andTechnology Website<http://www.mext.go.jp/component/b_menu/shingi/toushin/__icsFiles/afieldfile/2017/02/06/1381849_01_1.pdf>. (in Japanese)
117 Ministry of Education, Culture, Sports, Science and Technology, “Let’s establish an ICT environment at school: Four-year planfor establishing an environment to address IT utilization in education,” 2014. <http://jouhouka.mext.go.jp/school/pdf/2014ICTpanf.pdf>. (in Japanese)
118 Ministry of Education, Culture, Sports, Science and Technology, “Points for the revision of Course of study for Kindergarten andCourse of study for Kindergarten for Elementary and Lower Secondary School,” 2016.6.16.
<http://www.mext.go.jp/a_menu/shotou/new-cs/__icsFiles/afieldfile/2017/06/16/1384662_2.pdf>. (in Japanese)
119 Student Affairs Division, Elementary and Secondary Education Bureau, Ministry of Education, Culture, Sports, Science andTechnology, “About 2016 Survey of problematic behaviors of students and various problems in student guidance, such as nonattendance (preliminary figures),” 2017.10.26, p.64.
<http://www.mext.go.jp/b_menu/houdou/29/10/__icsFiles/afieldfile/2017/10/26/1397646_002.pdf>. (in Japanese)115

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from overseas, it is necessary to address differences in learning level through supplementary classes, which
impose a burden on teachers.
(2) Dealing with diverse learning contents
The Second Basic Plan for the Promotion of Education has as its directions developing creativity, achallenging spirit, leadership, Japanese identity, and language and communication skills and enhancing
excellent abilities and various personalities.120 However, dealing with these various tasks in education may
impose a great burden on teachers.
(3) Dealing with tasks other than lessons
In schools, there are tasks other than lessons, such as providing learning guidance, processing academic
results, managing grades and classes, and performing desk work. For example, in lower secondary schools,
38 minutes are spent per day on tasks related to the management of grades and classes (2016), which is anincrease of 11 minutes from 2006, while 38 minutes are spent on the processing of academic results (2016),
which is an increase of 13 minutes from 2006.121 In this regard, there has been a demand for the use of ICT
and related technology to reduce the burden of desk work in schools.122
3. Cases of the use of AI and ICT(1) Massive Open Online Courses
In the White Paper on Science and Technology 2016, Massive Open Online Courses (MOOCs) arecited as a method to “innovate the learning environment using ICT (to innovate educational methods)”.123
In Japan, JMOOC (Japan Massive Open Online Courses Promotion Council)124 has been launched, while
Khan Academy125 is in operation in the United States. As for books and conventional types of teachingmaterials for correspondence education and the like, students need to purchase and obtain teaching materials
for the subjects and domains they want to learn, which creates a great financial and procedural burden.
However, with MOOCs, many teaching materials are available 24 hours a day from a PC or a tablet, and theamount of such materials is increasing every day.126 This allows students to deal with various learning
contents at home. In addition, some teaching materials accommodate elementary to advanced levels or
multiple languages so that diverse learners can use them.
“Basic Plan for the Promotion of Education” (Cabinet decision on June 14, 2013), Ministry of Education, Culture, Sports, Scienceand Technology Website< http://www.mext.go.jp/en/policy/education/lawandplan/title01/detail01/sdetail01/1373808.htm >.
121 Ministry of Education, Culture, Sports, Science and Technology, “About the results of the survey of working conditions of teachers(preliminary figures),” 2017.4.28.
<http://www.mext.go.jp/b_menu/houdou/29/04/__icsFiles/afieldfile/2017/04/28/1385174_001.pdf>. (in Japanese)
122 For example, Central Council for Education, “Regarding the Revision of Courses of Study for Kindergarten, Elementary School,
Lower Secondary School, Upper Secondary School, and Schools for Special Needs Education (Report),
2008.1.17, p.140. Ministry of Education, Culture, Sports, Science and Technology Website<http://www.mext.go.jp/b_menu/shingi/chukyo/chukyo0/toushin/__icsFiles/afieldfile/2009/05/12/1216828_1.pdf>. (in Japanese)
123 Ministry of Education, Culture, Sports, Science and Technology (ed.), 2016 White Paper on Science and Technology, 2016,
pp.173-176. < http://www.mext.go.jp/component/english/__icsFiles/afieldfile/2017/04/14/1384513_011.pdf >.
124 JMOOC Website < https://www.jmooc.jp/en/>.
125 Khan Academy Website < https://www.khanacademy.org/>.
126 Dhawal Shah, “By The Numbers: MOOCS in 2017,” 2018.1.18. Class Central Website <https://www.classcentral.com/report/mooc-stats-2017/>.
120

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(2) Adaptive learning
COMPASS has developed a tablet for learning mathematics using AI. This tablet collects, accumulates,
and analyzes the answers of the student, processes leading to the answers, speed, degree of concentration,and degree of understanding and then gives questions according to the student’s level of understanding andstrong and weak points, which facilitates efficient learning. The system contributed to the improvement of
academic results in a trial experiment.127(3) Learning mentors
Gakken Group provides a service in which a mentor (learning supporter) offers advice on teachingmaterials appropriate for the student based on data obtained from analysis results of the degree ofunderstanding via AI using the academic results of the student as an input in the learning management
system.128 Surala Net has introduced a chat bot (automated conversation program) utilizing AI for its elearning system to help learners maintain the motivation for learning and to improve their ability toconcentrate by encouraging students according to their level of achievement.129(4) Streamlining of the scoring process
To streamline the process of scoring answers to description type questions, a research group, which
consists, for instance, of members from the University of California, has developed a system for recognizing
the letters of hand-written answers and automating the scoring process of answers using AI.130 In Japan,
automated systems for scoring answers to description-type questions are being studied.131
4. Issues surrounding the application of AI and robotics in education(1) Shortage of teachers and literacy
To utilize AI in schools, teachers who can use it must be first secured. However, in addition to massretirement from the aging of society, other problems concern the shortage of teachers due to job changes
among mid-career teachers132 and long work hours133 busyness among teachers and the shortage of teachers
127

Ministry of Internal Affairs and Communications (ed.), op.cit. (45), p.199.
<http://www.soumu.go.jp/johotsusintokei/whitepaper/ja/h28/pdf/n3300000.pdf>. (in Japanese)
128 “What is the new self-reliant individualized learning G-PAPILS?” Gakken’s new self-reliant individualized learning G-PAPILS
Website <https://www.g-papils.com/about/>. (in Japanese)
129 Shotaro Hamaguchi, “Supporting children with learning disabilities with e-learning, AI encouragement function,” 2017.3.2.
ITmedia Website <http://www.itmedia.co.jp/business/articles/1703/02/news141.html>. (in Japanese)
130 gradescope Website <https://gradescope.com/>; Tony Kontzer, “Shares Professors of the World, Rejoice: Gradescope Brings AI
to Grading,” 2016.9.12. NVIDIA Japan Blog Website <https://blogs.nvidia.co.jp/2016/09/12/gradescope-brings-ai-to-grading/>.
(in Japanese)
131 Masayuki Kameda et al., Pprototyping JS4 system for supporting the scoring of answers to short-answer description questions,”
Proceedings of the Twenty-third Annual Meeting of the Association for Natural Language Processing, 2017.3, pp.1137-1140.
<http://www.anlp.jp/proceedings/annual_meeting/2017/pdf_dir/C7-1.pdf> (in Japanese); Rintaro Terada et al., “Automatedscoring of answers to description type questions using neutral network,” Proceedings of the Twenty-two Annual Meeting of the
Association for Natural Language Processing, 2016.3, pp.370-373.
<http://www.anlp.jp/proceedings/annual_meeting/2016/pdf_dir/A2-1.pdf> (in Japanese); Tsunenori Ishioka, “Automatedgradingof
short
essay,”
Journalofthe
IEICE,
Vol.92
No.12,
2009.12,
pp.1036-1040.
<http://www.rd.dnc.ac.jp/~tunenori/doc/1036-1040.PDF>. (in Japanese)
132 Rin Natsume, “Why is there a shortage of teachers?” 2017.8.14. Education Tomorrow<https://edutmrrw.jp/2017/innovation/0814_teacher_shortage>. (in Japanese)
133 Central Council for Education’s Committee for Basic Plan for the Promotion of Education, op.cit. (116), p.8. According to the2013 OECD Teaching and Learning International Survey, the average working hours per week of a lower secondary school teacher

71who can utilize ICT.134 In terms of teachers’ literacy regarding AI and ICT, 84.0% are at the level of “being
able to utilize ICT for research of teaching materials and preparation of guidance,” while 75.0% can “utilize
ICT and teach during lessons,” and 66.7% “can teach students how to use ICT” (all figures are national
averages in 2017), which suggest that teachers skills in this area need improvement. However, the proportionof teachers who attended a “training program to improve teaching abilities utilizing ICT” was only 40.6%(2016 national average). 135(2) Establishment of ICT infrastructure and development of educational content
In terms of ICT infrastructure in schools, 6.2 students (national average) shared one computer for
educational purposes as of March 2016.136 Thus, environments for using ICT have not been sufficiently
established. In terms of the introduction of so-called “digital school textbooks,” there are issues associatedwith the establishment of ICT facilities and improvement of teachers’ abilities to teach. In addition, there are
institutional problems with paper-based school textbooks, such as the textbook authorization system and thesystem for the free provision of textbooks.137(3) Credibility and safety of AI
Credibility and safety are some issues with the use of AI in the domain of education. In terms ofcredibility, for example, it is difficult for AI to understand natural languages and the meaning of sentencesat this point.138 Therefore, it is difficult for now for AI to perform interpretation and scoring for teachers
without any help from people.
In terms of the issue of safety, program bugs (malfunctions) or security vulnerabilities may accidentally
occur with highly complex AI, which may lead, for instance, to the loss or breach of personal information of
students or cause robots to go out of control.139 Technology to allow other AI to fix bugs is now being
studied as a measure against such problems, although it will not be designed specifically for the domain of

in Japan was 53.9, which is the highest among the countries and regions that participated in the survey (the average of the surveyedcountries and regions was 38.3).
134 Japan Association for Promotion of Educational Technology (JAPET) & Microsoft Japan, “Survey of the use of ICT at school[data collection],” 2011, p.31. <http://www2.japet.or.jp/ict-chosa/ict_chosa_data.pdf>. (in Japanese)
135 Ministry of Education, Culture, Sports, Science and Technology, “Results of the 2016 survey of situations of the informatizationof school education (Summary) (as of March 2017), 2018.2, pp.23-25, 30.
< http://www.mext.go.jp/component/a_menu/education/micro_detail/__icsFiles/afieldfile/2018/03/07/1399330_01.pdf> (inJapanese).
136
Ministry of Education, Culture, Sports, Science and Technology, “Results of the 2015 survey of situations of the informatizationof school education (Summary) (as of March 2016), 2016.10, p.2.
<http://www.mext.go.jp/component/a_menu/education/micro_detail/__icsFiles/afieldfile/2016/10/13/1376818_1.pdf>.
(inJapanese).
137 Committee for reviewing the positioning of “digital textbooks,” “Final report of the Committee for reviewing the positioning of‘digital textbooks’,” 2016.12.
<http://www.mext.go.jp/b_menu/shingi/chousa/shotou/110/houkoku/__icsFiles/afieldfile/2017/01/27/1380531_001.pdf>.(inJapanese)
138 Noriko Arai, “Abilities required in the era when we coexist with AI: What has been revealed by Tou-robo’s reading skills,”
2017.6.28. Manabinoba.com Website <https://www.manabinoba.com/event_reports/016031.html>.
139 Not an educational robot but a security robot in operation injured a child at a shopping center. Lilian Kim, “Parents upset after
Stanford Shopping Center security robot injures child,” ABC NEWS, 2016.7.11. <http://abc7news.com/news/parents-upset-afterstanford-mall-robot-injures-child/1423093/>.

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education.140
Hideaki Iwahori, The University of Tokyo

Ⅴ Hospitality
1. What is hospitality?
The number of those who work in the tertiary sector was 24,510,000 (46.6% of all employed workers)
in 1970, but the figure increased to 39,620,000 (71.0%) in 2015 in Japan. The trend toward a service
economy is expanding in terms of the employment structure.141 A number of categories of businesses areincluded in the tertiary sector, and hospitality services are diverse. However, this chapter will discuss
employment and labor in the lodging industry, food and beverage industry, sales industry (retail andwholesale), and leisure industry (entertainment industry), where the quality of hospitality significantly affects
customer evaluations.142 The number of employed workers has been decreasing for the past five years since2010 in the lodging industry, food and beverage industry, wholesale industry, retail industry, livelihoodrelated service industry, and entertainment industry.143
2. Points of argument and issues pertaining to hospitality(1) Increase of “mentality,” such as omotenashi
Some note that “service” is used to refer to after-sales service or claim handling, but it now is blended
with “omotenashi” or “hospitality,” a concept based on the spirit of dedication. Here, not only uniform
services appropriate for the price paid but also the degree of satisfaction and emotional aspects of those who
receive hospitality are considered important.144
Sociologist Arlie R. Hochschild noted labor such as “omotenashi,” in which workers are expected toregulate their emotions in accordance with the emotions of customers “emotional labor,” as being among all
the types of labor performed by workers.145 Hochschild notes that as the added value of technology has
decreased and become a commodity, businesses have begun to require this “omotenashi” of their workers as
Joji Okada, “Can bugs be fixed automatically!? The present of automated program-fixing technology,” 2016.4.14. NTT DATA
Website <http://www.nttdata.com/jp/ja/insights/trend_keyword/2016041401.html> (in Japanese); Larry Hardesty, “Recognizing
correct code: Automatic bug-repair system fixes 10 times as many errors as its predecessors,” 2016.1.29. MIT News Website<http://news.mit.edu/2016/faster-automatic-bug-repair-code-errors-0129>.
141 Ministry of Health, Labour and Welfare, Analysis of the Labour Economy 2013 Summary, 2013. <http://www.mhlw.go.jp/english/wp/l-economy/2013/dl/2013summary.pdf >; Bureau of Statistics, Ministry of Internal Affairs andCommunications, “Summary of the results of the Basic Complete Tabulation on Working Conditions of the 2015 National
Census of Japan,” 2017.4.26, p.12. <http://www.stat.go.jp/data/kokusei/2015/kekka/kihon2/pdf/gaiyou.pdf> (in Japanese). The
trend toward a service economy is seen not only in the employment structure by industry but also in the employment structure by
occupation.
142 Chiharu Iwai, “A consideration of politeness in materials for teaching hospitality,” Language and culture, Vol.16, 2017.3, p.28.
<http://repository.osakafu-u.ac.jp/dspace/bitstream/10466/15188/1/2016000205.pdf> (in Japanese).
143 Bureau of Statistics, Ministry of Internal Affairs and Communications, “Summary of the results of the Basic Complete
Tabulation on Working Conditions of the 2015 National Census of Japan,” 2017.4.26, p.12.
<http://www.stat.go.jp/data/kokusei/2015/kekka/kihon2/pdf/gaiyou.pdf> (in Japanese).
144 Akira Yasuda, “Service and hospitality: Its genealogy and structure,” Hospitality Management, Vol.2 No.1, 2011.3, pp.93-103.
<https://ci.nii.ac.jp/els/contentscinii_20171203183407.pdf?id=ART0009827487> (in Japanese).
145 A.R. Hochschild , The Managed Heart: Commercialization of Human Feelings, 1983.
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added value, and she argues that this invisible labor has become a psychological burden for workers,
introducing actual cases in the domains of hospitality, elderly care, and education. 146 Supporting thisemotional labor with technology is expected to improve work environments.
(2) Utilization of AI and robotics in hospitality
With “functional service,” it is easy to provide uniform service appropriate for the price paid and is easy
to manualize such service, among various tasks in hospitality. Unlike this functional service, it is said that“emotional service,” which creates customer satisfaction by providing omotenashi or making customers feel
special, is a task that is difficult for AI to replace.147 This is because responses must be considered inaccordance with the context of each person, and goals must be explored through communication with thecustomers, among others. Having AI understand the context of individuals is an area still being developed
even in today’s AI technology. Expecting this to be solved in the next few years is not realistic.
Therefore, in the current situation, instead of replacing hospitality as it is, ways are now being exploredto divide hospitality services traditionally provided by people into those that can be replaced by AI androbotics and those that people perform better in order to mechanize and automate the former type of
hospitality services and have humans focus on the latter.148
3. Cases(1) Automation through the introduction of vending machines
Vending machines can be referred to as “robots” that can provide services without human intervention.
In recent years, vending machines equipped with image recognition or voice recognition functions have been
installed. Among hospitality services that listen to customers’ requests and take previously defined responses
according to those requests in the sales business, those that do not require exceptional responses can behandled by vending machines set up with several request patterns. It is expected that the introduction ofvending machines can reduce labor costs, allow 24-hour operation, and enable responses in multiple
languages, such as English and Chinese.
As cases in Japan, convenience stores sell onigiri (rice balls) and bento (boxed lunch) using vending
machines,149 and McDonald has introduced an unmanned ordering machine at some of its stores.150 In somebusiness hotels, automated check-in and check-out machines have been installed. There are also somevending machines that show recommended products customized for each person through face recognitionor voice recognition functions.151

146

Same as above.
Michael Chui et al., “Where machines could replace humans—and where they can't (yet),” 2016.7. McKinsey & Company
Website <https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/where-machines-could-replace-humansand-where-they-cant-yet>.
148 Hirotaka Osawa et al., “Analysis of Robot Hotel: Reconstruction of Works with Robots,” IEEE International Symposium on
Robot and Human Interactive Communication, 2017, pp.219-223.
149 “Vending machine convenience store (ASD), Family Mart Website <http://www.family.co.jp/company/asd.html> (in Japanese).
150 Daichi Goto, “McDonald increases sales growth and stock price with self-checkout machines,” 2017.6.26. MyNavi News
Website <https://news.mynavi.jp/article/20170626-a120/> (in Japanese).
151 Maki Miyamoto, “Vending machine with marketing brain shows up in Shinagawa Station, 47-inch touch panel operation,”
2010.8.10. ITmedia Website <http://www.itmedia.co.jp/news/articles/1008/10/news080.html> (in Japanese).
147

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(2) Unmanned operation through the introduction of human-like robots and mobile robots
Human-like robots and mobile robots with appearances that allow for closer interaction between
machines and people have been introduced in airports and in the lodging industry. An accommodation
facility called “Henn-na Hotel” represents a case where robots are used to provide hospitality services.152
Robots perform tasks related to reception, cloakroom, luggage loading, cleaning, and concierge services.
Because people handle customers when robots cannot handle them alone, the hotel can keep the number of
employees and labor costs low.153
At “Henn-na Hotel,” robots also replace humans in performing not only “functional services,” such as
check-in procedures, but also greeting and concierge services as an attempt to replace “emotional services.”
At this point, the level sufficient to replace emotional services of humans has not yet been achieved, but wecan expect a reduction of “emotional labor” associated with employees’ “emotional services” from having
robots take a central role in hospitality and having employees assist them.154 On the other hand, it has been
noted that employees using robots or working together with robots need to understand the structure of robots,
and they need skills in giving instructions to robots without doing the actual tasks (reception or cleaning)that robots do and in making evaluations and judgments. Customers may also need literacy (understanding
and readiness) as to how they should handle interactions with robots.
4. Issues
The following issues are noted in relation to the introduction of AI and robotics in hospitality.
(1) Technological issues and establishment of the environment
The advantage of using AI and robotics in hospitality is the capability of providing customized
hospitality services to individuals: however, there are still some problems regarding the recognition accuracyof customers’ likeness and voice. For example, voice recognition technology has improved from theseparation of sound sources of speakers using multiple microphones and the accumulation of vast volumesof data using smart phones, but there are still problems when it comes to recognizing conversations in a reallife environment where conversations of multiple people and noises exist.155 The technology sometimes
cannot recognize information correctly due to biased data learned by AI, in which cases people need toprovide assistance.
It is also necessary to create an environment where robots can easily move around, in terms of humanlike robots and mobile robots. “Henn-na Hotel” is a totally barrier-free facility so that robots can move aroundeasily within the building. There is a Japanese inn that invested 70,000,000 yen to reduce the food-serving
burden of room attendants and introduce an automated food serving system.156
AIR, “A visit to “Henn-na Hotel”: Ever-changing workplace,” Information Processing, Vol.57 No.11, 2016.10, pp.1078-1083(in Japanese). “Henn-na” means “Wired.”
153 ““Henn-na Hotel” providing robot hospitality will go global, Second hotel will be built in Maihama, Chiba, The next ones will
be in Osaka and Nagoya near popular theme parks,” 2016.8.4. Travel Voice Website <https://www.travelvoice.jp/2016080471802> (in Japanese).
154 AIR, op.cit. (152).
155 See “IV Speech User Interface” of Part 1 of this report.
156 Sadahiko Oda, “Omotenashi of long-established Japanese inn “Kagaya” supported by robots,” Chuo Koron, Vol.130 No.4,
2016.4, pp.89-94 (in Japanese). In Japan, some ryokan (luxurious inn) provides dinner room service.
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(2) Privacy
It is necessary to be careful with privacy when storing dialogues with customers over a long period oftime to improve the quality of hospitality.157 In particular, the “recommendation” function has been
already realized in internet ads, for instance, using stored data, but there exists an issue of privacy. For
example, a supermarket in the US that analyzed the purchase history of a teenage girl as “predicted
pregnancy” and “recommended” products related to pregnancy became problematic. It turned out later that
she was actually pregnant.158
Thus, when AI describes a person based on his or her purchase history (e.g., profiling), businesses canobtain information about the person’s status and taste, but other people may also obtain such information
through displayed ads. In relation to this problem, the General Data Protection Regulation (GDPR),159
which was adopted in 2016 to reinforce the protection of personal data in the EU and will come into effecton May 25th, 2018, will include “the right not to be subject to profiling.
(3) Reconsideration of values provided through “hospitality”
When machines replace some hospitality services, workers will be free of “emotional labor” but may
have fewer opportunities to receive “gratitude” or “smiles” from customers.160 Some tasks may be replacedby robots, but there may be some value that only humans can provide. Therefore, when introducing AI androbotics in hospitality, it will be important to clarify what kind of value they should offer customers, whattasks will be performed by robots, and what will be done by humans.
Hirotaka Osawa, University of Tsukuba
Arisa Ema, The University of Tokyo

Ⅵ Transportation/Mobility
1. Shortage of drivers in the transport industry and related issues
Due to the expansion of demands for e-commerce services using the internet, the number of parcels
handled by package delivery services has been steadily growing recently, reaching 4.02 billion in 2016, anincrease of 270 million parcels (7.3%) from the previous year in Japan.161 In the road freight transportindustry, the wage level is relatively low compared to the average among all industries, but the work

157

AIR, op.cit. (152).
Kashmir Hill, “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did,” Forbes, 2012.2.16.
<https://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/>.
159 “REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 27 April 2016 on theprotection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing
Directive 95/46/EC (General Data Protection Regulation),” Official Journal of the European Union, L119, 2016.5.4, pp.1-88.
<http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679&from=EN>.
160 AIR, op.cit. (152).
161 Logistics Policy Planning, Logistics Policy Division, Cargo Transport Division, Road Transport Bureau, Ministry of Land,
Infrastructure, Transport and Tourism, “About the number of parcels delivered in 2016,” 2017.7.28. Ministry of Land,
Infrastructure, Transport and Tourism Website <http://www.mlit.go.jp/report/press/jidosha04_hh_000136.html> (in Japanese).
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environment is poor; truck drivers have to work long hours, and the industry is aging.162 To address thisissue in the private sector, Yamato Transport reported to withdraw from the same day delivery service of
Amazon, an internet shopping giant.163 The work environment of bus drivers and taxi drivers, including thewage level and working hours, is also worse than the average among all industries.164
Against this background, a shortage of drivers is becoming a problem, as over half of the drivers in thetransport industry (truck, bus, and taxi) in Japan have felt a manpower shortage.165 There is a concern thatthe shortage of drivers and the work environment in the transport industry may worsen in the future due topopulation aging and the declining birthrate. Some note that the mass transportation system may decay
mainly in rural areas, which will result in a reduction in means of transportation for the elderly.166 “Last one
mile”167 has become a problem here in relation to the redelivery of parcels and mobility from the closest
station to home.
2. Reduction of drivers’ burden and the operation of new mobility services
To improve the work environment of drivers, the Ministry of Health, Labour and Welfare formulated
the “standard for improving working hours” of bus, truck, and tax drivers.168 In addition, technologies are
now being developed to determine whether drivers are in a state fit to safely drive based on their sleepinessand smart phone use through “driver concentration sensing technology.”169
To address the shortage of drivers, there have been discussions on unmanned operations using selfdriving vehicles. To realize self-driving vehicles, the Cabinet Office formed the “Committee for promoting
automated driving system” in 2014, and the Ministry of Land, Infrastructure, Transport and Tourism,
Ministry of Economy, Trade and Industry, Ministry of Internal Affairs and Communications, and National
Police Agency have been conducting research and development and discussing institutions related.170 The
Cabinet Office’s “Cross-ministerial Strategic Innovation Promotion Program (SIP) cites “automated driving
systems” as one of the issues. The program is working not only on developing technology related to selfdriving cars but also on creating high-accuracy maps for achieving high-accuracy position estimation. It is

Japan Trucking Association, “Japan’s truck transport industry, Current status and problems 2017,” 2017.8, p.15.
<http://www.jta.or.jp/coho/yuso_genjyo/yuso_genjo2017.pdf> (in Japanese).
163 “Yamato withdraws from same day delivery service, Yamato’s direction, turning point for online shopping”, Nihon Keizai
Shimbun, 2017.4.7, p.1 (in Japanese).
164 “2016 current statuses of wages and working hours of taxi drivers,” 2017.4.18, p.7 (Table 7). Japan Federation of Hire-Taxi
Associations Website <http://www.taxi-japan.or.jp/pdf/toukei_chousa/tingin28.pdf> (in Japanese).
165 Ministry of Health, Labour and Welfare(ed.), 2017 White Paper on Measures to Prevent Karoshi, etc., 2017, pp.4. „<http://fpcj.jp/wp/wp-content/uploads/2017/11/8f513ff4e9662ac515de9e646f63d8b5.pdf >.
166 Yusuke Ikeda, “Current statuses of self-driving technology, etc.,” Jurist, No.1501, 2017.1, p.16 (in Japanese).
167 “Last one mile” (last mile in English) means the route from the closest distribution center to the client in parcel delivery and theroute from the closest station or bus stop to home in mobility. Even if there are no problems in getting to the distribution center orthe closest station, there may be redelivery within the last section or a loss of means of transportation except on foot.
168 “Standards for improving working hours of vehicle drivers,” Ministry of Health, Labour and Welfare Website<http://www.mhlw.go.jp/stf/seisakunitsuite/bunya/koyou_roudou/roudoukijun/gyosyu/roudoujouken05/index.html> (inJapanese).
169 OMRON Corporation, “Developing the world’s first ‘leading-edge AI-equipped vehicle-mounted censor’,” 2016.6.6. OMRON
Website <http://www.omron.co.jp/press/2016/06/c0606.html> (in Japanese).
170 Masahiro Kobayashi, “Legislative issues toward realization of autonomous vehicles,” Journal of information processing andmanagement, Vol.60 No.4, 2017.7, p.241. <https://www.jstage.jst.go.jp/article/johokanri/60/4/60_240/_pdf/-char/ja> (inJapanese).
162

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also conducting research on rules related to mutual recognition and communication among drivers, vehicles,
and pedestrians171 and an empirical experiment related to truck “platooning” on highways.172
3. Cases
Unmanned operations using self-driving cars (fully automated driving) are still at the stage of empiricalexperiments. However, transportation and mobility services using smart phone apps and IoT are now being
introduced as measures for addressing last mile, and they are expected to streamline the tasks oftransportation and mobility.
(1) Vehicles for ride share using AI
Empirical experiments for enabling AI to predict mobility demands in real-time and allocating
vehicles (buses, taxis) available for ride sharing according to demand without fixing routes are being
conducted; it is assumed that the allocation of unmanned self-driving cars may become a reality in the
future.173 This will allow drivers to drive their cars according to demand while reducing vacant driving andtime waiting for passengers; thus, it is expected to reduce drivers’ burdens.
(2) Delivery of parcels using IoT when the receivers are away
In November 2017, Amazon launched a service in the US for delivering parcels to homes when
receivers are away, using an IoT device (smart lock) to open and lock the front door of homes via theinternet.174 In Japan, Linough, Inc. announced that it will launch parcel delivery and housekeeping services
using smart lock in February 2018, which is expected to solve the issue of redelivery.175 This service lets
other people in the house. Therefore, it will use a cloud camera to check images around the front door of ahouse from a smart phone and another indoor door with a lock installed in the back of the front door to ensure
security.176(3) Drones
The Civil Aeronautics Act (Act No. 231 of 1952) was amended in 2015, stipulating rules regarding
permission and application when flying unmanned aerial vehicles and flying-allowed zones. In 2015, theDirector General for Science, Technology and Innovation, Cabinet Office, “Cross-ministerial Strategic Innovation Promotion
Program (SIP) Research and development plan of self-driving system,” 2017.9.28.
<http://www8.cao.go.jp/cstp/gaiyo/sip/keikaku/6_jidousoukou.pdf> (in Japanese).
172 “We will begin the world’s first experiment of truck “platooning” on highways using CACC: Empirical experiment of truck“platooning” on highways to realize mobility revolution,” 2018.1.12. Ministry of Economy, Trade and Industry Website<http://www.meti.go.jp/press/2017/01/20180112002/20180112002.html> (in Japanese).
173 Mirai Share, “Spread of a new transportation service using Smart Access Vehicle (SAV),” 2017.3. Ministry of Economy, Tradeand Industry, Hokkaido Bureau of Economy, Trade and Industry Website<http://www.hkd.meti.go.jp/hokim/20170217/data05.pdf> (in Japanese).
174 Joan E. Solsman, “Amazon Key hack could make you clueless in a home invasion,” 2017.11.16. CNET Website <https://www.cnet.com/news/amazon-key-hack-camera-security/ >.
175 “Project ‘Service coming into home’ even when you are away from home is launched in “Genia Omori-nishi” in Ohta-ku, Tokyo,[Japan’s first rental apartment cooperating with parcel delivery and housekeeping services], Partnering with 5 businesses, including
PalSystem,” 2018.1.30. Linough Website <https://linough.com/> (in Japanese).
176 Wakako Mukohata, “Can this be Japanese version of Amazon Home>? New project using smart lock so that you can receive
parcel delivery and housekeeping services even when you are away from home,” 2018.1.30. TechCrunch Website<http://jp.techcrunch.com/2018/01/30/linough-starts-the-project-for-secure-delivery-and-house-keeping-services-while-not-athome/> (in Japanese).
171

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Ministry of Land, Infrastructure, Transport and Tourism began discussions regarding the realization of parcel
delivery using drones as early as 2018 to address the shortage of manpower in the transport industry.177
Drones are expected to be used not only in urban areas but also in remote island areas and underpopulatedareas, at times of disaster, and for freight transport within warehouses.178
In 2015, experiments were carried out for transporting drugs and medicine (supposedly) and for
performing disaster surveillance in Kannonji City, Kagawa Prefecture, as part of the “Setouchi Kamome
Project,” in which private businesses are studying the use of drones in remote island areas, secluded areas,
and mountainous areas.179
On the other hand, because the weight should be limited to about 10 kilograms when using drones fortransportation, economic feasibility180 is considered one of the issues, in addition to the problem of noise.181
The “Council for establishing an environment for flying drones via government-private sector collaboration,”
which consists of officials of relevant ministries and personnel of relevant industries, has presented a roadmap for the future in relation to the development of technology and establishment of the related environment
with the assumption of aerial vehicles flying without pilots or any assistance in order to promote the use ofdrones.182
4. Issues(1) Legal issues
In local areas, ride-share services using private vehicles are anticipated as a means of transportation thatwill complement public transportation.183 However, because the Ministry of Land, Infrastructure, Transport
and Tourism noted that such services may be in violation of the Road Transportation Act (Act No. 183 of
1951),184 which prohibits so-called “unlicensed taxi” services, these ride-share services are provided only insecluded areas at this point as an exception.185 On the other hand, ride-share services where drivers receive
only the actual expenses from users are considered outside the scope of the act,186 and therefore, people arepaying attention to how the services are going to expand in the future.
Ministry of Land, Infrastructure, Transport and Tourism, “Use of drones in the domain of logistics,” Ministry of Land,
Infrastructure, Transport and Tourism Website<http://www.mlit.go.jp/seisakutokatsu/freight/seisakutokatsu_tk_000024.html> (in Japanese).
178 Logistics Policy Division, Policy Bureau, Ministry of Land, Infrastructure, Transport and Tourism, “2015 Report of tasks
including surveys pertaining to the use of drones in logistics,” 2016.3, pp.6-7. <http://www.mlit.go.jp/common/001136816.pdf>.
179 Same as above, pp.19-20 (in Japanese).
180 “Drone logistics, issue of economic feasibility,” Mainichi Shimbun (western region version), 2017.9.2, p.22 (in Japanese).
181 Jin Kodama, “NASA research “Noises of drones put more stress on human than cars, Measures against noise is necessary of thewide use of drones,” 2017.7.20. roboteer Website <https://roboteer-tokyo.com/archives/9727> (in Japanese).
182 Council for establishing an environment for flying drones via government-private sector collaboration, “Roadmap for industrial
revolution in the air: Technological development and environment establishment for safe use of drones,” 2016.4.28. <http://www.meti.go.jp/english/policy/mono_info_service/robot_industry/downloadfiles/uasroadmap.pdf >.
183 For example, “Effective use of the internet to make local transportation more convenient,” Nihon Keizai Shimbun, 2015.12.1, p.2(in Japanese).
184 “MLIT orders share-ride services to stop operations for providing possible unlicensed taxi service in Fukuoka’s experiment,”
Nihon Keizai Shimbun, 2015.3.5, p.38 (in Japanese).
185 In Tango City, Kyoto Prefecture, the service is operated based on the “fare-paying conveyance of passengers in areas where no
public transportation is available,” as defined in Article 49, Section 1 (2) of Ordinance for Enforcement of the Road Transportation
Act (Ordinance of the Ministry of Transport No. 75 of 1951. “What is “mutual support transportation”?” Kibaru! Furusato Tangocho Website <http://kibaru-furusato-tango.org/about-sasaeai/> (in Japanese).
186 The ride-share service is operated in Nakatombetsu, Hokkaido, as a fare exempt from the Road Transportation Act. “Experimentof Nakatombetsu share-ride project,” 2017.8.21. Nakatombetsu-cho Website
177

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Ride-share services using private vehicles are performed by the driver of the private car who signed acontract with the service provider. Issues related to the legal rights and obligations of contracted drivers havebeen noted, such as the issue of safety management associated with the responsibility taken by the contracted
driver, 187 not by the service provider, and the issue of the provision of welfare benefits to contracted
drivers.188
In terms of self-driving cars, civil and criminal liability189 in the event of an accident and insurance
systems190 are considered problems.191(2) Privacy
The issue of privacy can occur when it comes to private information associated with people’s movement.
For example, the providers of mobility services may keep or use the location information of users, whichraises concerns regarding the invasion of privacy
Drones equipped with cameras can also recognize certain people based on images taken from the air,which also raises concern regarding the invasion of privacy. The Ministry of Internal Affairs andCommunications has warned about the handling of images taken by drones on the internet.192
Arisa Ema, The University of Tokyo

Ⅶ Agriculture
1. Dealing with the shortage of agricultural workers and the use of AI and robotics(1) Dealing with the shortage of agricultural workers
In the “Basic Plan for Food, Agriculture and Rural Areas” (Cabinet decision on March 31, 2015), six
perspectives are provided in relation to the current perception of food, agriculture, and rural areas: (1) the
effects of aging and population decline on food, agriculture and rural areas; (2) the prospects of global food<http://www.town.nakatombetsu.hokkaido.jp/docs/2016081800017/> (in Japanese). See the following material as well: Ministryof Economy, Trade and Industry, “Handling of the Road Transportation Act in relation to mid- to long-distance share-ride matching
services has been clarified: Use of the System to Eliminate Regulatory Gray Zones in Act on Strengthening Industrial
Competitiveness, “2017.5.18. Ministry of Economy, Trade and Industry Website<http://www.meti.go.jp/press/2017/05/20170518001/20170518001.html> (in Japanese).
187
Yoshio Tsukuda, “Obstacles hindering the spread of ‘share-ride’,” 2016.8.12 Yomiuri Online<http://www.yomiuri.co.jp/fukayomi/ichiran/20160810-OYT8T50091.html?page_no=2> (in Japanese).
188
David Priest, “Uber gives in, grants some benefits to British drivers,” 2017.4.27. CNET Website<https://www.cnet.com/news/uber-brings-benefits-to-british-drivers/ >.
189 For example, “Feature: Autonomous driving and civil liability,” Jurist, No.1501, 2017.1, pp.13-55; Yuka Nakagawa, “Criminal
liability of drivers and manufacturers pertaining to autonomous driving: Problems arising from the introduction of autonomous
driving and future directions,” Chukyo Lawyer, No.27, 2017.9, pp.15-29. <https://www.chukyo-u.ac.jp/educate/lawschool/chukyolawyer/data/vol027/02_nakagawa.pdf> (in Japanese).
190 For example, General Insurance Association of Japan New Risk PT, “About legal issues of autonomous driving,” 2016.6.
<http://www.sonpo.or.jp/news/file/jidou_houkoku.pdf> (in Japanese).
191 Trends and problems of autonomous driving technology: 2017 report of the project for investigating science and technology(Survey material 2018-4), National Diet Library, 2018 (in Japanese), addresses technological trends related to autonomous driving
and its various issues.
192 Ministry of Internal Affairs and Communications, “Warning about the handling of images on the internet taken by drones fromthe air,” 2015.4.28. Ministry of Internal Affairs and Communications Website<http://www.soumu.go.jp/menu_kyotsuu/important/kinkyu02_000189.html> (in Japanese).

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supply and demand as well as progress of globalization; (3) the diversification of issues concerning therelationship between consumers and food; (4) changes in the structure of agriculture and rural areas,
including farmers who support agriculture; (5) diverse possibilities of agriculture and rural areas; and (6) the
status of restoration/reconstruction from the Great East Japan Earthquake.193 Agriculture is closely linked
with the maintenance and management of farm lands and agricultural water, food processing, anddistribution, but in this chapter will focus on employment and labor associated with the production of crops
and livestock raising.
While there are various types of agriculture, such as rice growing, dry-field farming, dairy farming,
livestock raising, and fruit farming, the labor force required varies by period. Farmers need to hire a massive
labor force during the harvesting season of seasonal crops in particular.194 Because many of the tasks are
based on experience or intuition, it takes a considerable amount of time for new farmers to become effective
workers.195 The number of core persons mainly engaged in farming, whose daily job is farming, among the
population engaged in farming has decreased by 22% (from 2,240,000 to 1,750,000) over the past 10 years.
The number of regular workers hired for a period of over 7 months increased 1.6 times (from 61,094 to
99,393), but the agricultural labor force is shifting from household members (e.g., relatives who live in thesame household) to employees.196
To address the labor force shortage caused by the aging of farmers and people leaving farming, theMinistry of Agriculture, Forestry and Fisheries is working of securing new farmers, mainly young people,
by supporting them through training programs before they become farmers and supporting the establishmentof businesses after they become farmers as a project for investing in next-generation farmers. This hasapparently paid off.197 In 2016, the number of new farmers exceeded 60,000 for the first time in 6 years, andthe number of new farmers aged 49 or younger was the highest since 2007.
(2) Establishment of an environment toward the use of AI and robotics
AI and robotics are anticipated as new measures for complementing the labor force shortage andimproving labor productivity.198 Safety is also expected to improve by using AI and robotics in dangerous
tasks, such as pesticide spraying on slopes or mowing.199
In 2013, the Ministry of Agriculture, Forestry and Fisheries formed the “Study group for achieving
smart agriculture,”200 with cooperation from robotics and IT businesses, farm equipment manufacturers, andrelevant ministries, to discuss methods for promoting the use of AI and robotics in agriculture (smart
193

Summary of the Basic Plan for Food, Agriculture and Rural Areas (Cabinet decision on March 31, 2015). Ministry of Agriculture,
Forestry and Fisheries of Japan Website < http://www.maff.go.jp/e/policies/law_plan/attach/pdf/index-2.pdf >.
194 Sumito Yasuoka, “Promotion of smart agriculture,” Journal of the Robotics Society of Japan, Vol.35 No.5, 2017.6, p.362.
195 Ministry of Agriculture, Forestry and Fisheries of Japan, “About acceleration of smart agriculture using AI and IoT (draft), (Fifthmeeting of the study group for achieving smart agriculture, material 3-1), 2016.11, p.2.
<http://www.maff.go.jp/j/kanbo/kihyo03/gityo/g_smart_nougyo/attach/pdf/kenkyu_kai05-6.pdf> (in Japanese).
196 Ministry of Agriculture, Forestry and Fisherie, Summary of the Annual Report on Food, Agriculture and Rural Areas in Japan,
2017.5, p.6. < http://www.maff.go.jp/e/data/publish/attach/pdf/index-57.pdf>.
197 Same as above, pp.6, 17.
198 Same as above, p.16.
199 Kenji Imou, “Automation of bio production machinery and the use of robotics,” Journal of the Robotics Society of Japan, Vol.35
No.5, 2017.6, p.378 (in Japanese).
200 Ministry of Agriculture, Forestry and Fisheries of Japan, “Study group for achieving smart agriculture,” Ministry of Agriculture,
Forestry and Fisheries of Japan Website <http://www.maff.go.jp/j/kanbo/kihyo03/gityo/g_smart_nougyo/index.html> (inJapanese).

81agriculture) and to devise guidelines. In November 2016, the study group sorted out the possibilities of using
AI and robotics to address problems facing agriculture. To address the decline in the number of farmers andlabor force shortage, it laid out a direction to promote efforts (1) to promote significant labor saving throughthe use of robots for various tasks and (2) to encourage the use of farming practices in which anyone caneasily engage (through the introduction of a system where people can learn the know-how of experienced
farmers in a short period of time and conduct image analysis of disease and pests).201
2. Cases(1) Autonomous vehicles, fruit and vegetable harvesting robots, and drones
Autonomous vehicles made with conventional agricultural vehicles, such as tractors, rice planters, andcombines, equipped with location measuring equipment and self-steering equipment have not yet been
widely accepted, not because of technical problems, but because a system concerning liability in the eventof accidents has not been fully established.202
In terms of fruit and vegetable harvesting robots, for example, strawberry harvesting robots need tomove on a rail within the facility, and they cannot be introduced to existing facilities. Therefore, some notethat it is necessary to design entire facilities, not just robots, on the assumption that robots will be doing
growing and harvesting tasks.203 Studies have also been conducted on a cultivation method for making tree
forms straight so that robots can be utilized at low cost.204 Either way, the work speed is slower than thatwith human workers, and robots are expensive; accordingly, these methods remain at the experimental phaseat this point.205
In terms of drones, technology has been developed for automatically measuring the growth level anddetecting disease and pests, which have been conventionally performed by humans with the use of airborne
images and image recognition technology. Such technology will allow tasks to be streamlined.206 Pesticide
spraying using drones has been carried out in Japan since the 1980s; it has been estimated that the numberof registered drones for such purposes in Japan exceeds 2,500.207(2) Gathering, analysis, and use of agricultural information through the cloud
Cloud services can make agriculture easy for anyone to engage in by gathering, analyzing and using
mobile devices and sensors and visualizing the experience of experienced farmers. For example, Fujitsu’s“Akisai” cloud for the food and agricultural industry helps to increase the yield ratio by thoroughly enforcing
tasks at proper times, to reduce total work hours through analysis of work processes, to allow farmers toreceive advice from farming management advisers, and to predict the emergence of disease and pests. In the
future, Fujitsu is looking to establish an efficient production model based on the accumulation and analysis
201

Ministry of Agriculture, Forestry and Fisheries of Japan, op.cit. (196)Imou, op.cit. (199), p.377.
203 Same as above.
204 Yasuoka, op.cit. (194), p.365.
205 Naoshi Kondo, “Sensing and robot technology in food production facilities for the era of 9 billion people,” Journal of the Robotics
Society of Japan, Vol.35 No.5, 2017.6, p.381 (in Japanese).
206 Aya Sugiura, “High-speed field phenotyping through drone airborne images,” Journal of the Robotics Society of Japan, Vol.35
No.5, 2017.6, p.369 (in Japanese).
207 Shinji Suzuki, “Technological and institutional directions for the safe use of drones,” Journal of the Robotics Society of Japan,
Vol.34 No.1, 2016.1, p.24. <https://www.jstage.jst.go.jp/article/jrsj/34/1/34_34_24/_pdf/-char/ja> (in Japanese).
20282

of data, to standardize information, and to make data open.208 The system is expected to allow new farmers
to engage in tasks with a sense of security by allowing them to use objective data.
(3) Cattle feeding management and ranch operation management using wearable devices
Farmnote, an IT venture in Hokkaido, developed a wearable device called “Farmnote Color,” which isput on a cattle’s neck and gathers data in real time. “Farmnote Color” gathers the behavioral data of cattle,
analyzes it using AI, identifies the estrus symptoms or diseases of cattle, and automatically sends information
regarding abnormal cattle and the like to smartphones and similar devices. 209 The company provides“Farmnote,” which is a cattle population management system using “Farmnote Color”, to farmers. Over2,000 farmers already use the system.210 The company also holds seminars to contribute to the formation of
farmers’ community their systems. 211 The cattle population management system has a ranch operation
management function, and it is capable of visualizing financial health, sales, cost and allows multiple people
to communicate with each other in real time.212
3. Points of argument over the use of AI and robotics in agriculture(1) Establishment of an environment for facilitating the use of data
To use the data obtained from wearable devices and images in actual workplaces, it is necessary to use
mobile devices, such as smartphones, to record and view data.213 To do so, it is important to secure a certain
level of communication speed even in rural areas and to establish a communication environment foraccessing the cloud. To predict or analyze growth, a certain amount of data must be accumulated. However,there are some types of data that can be obtained only in certain seasons, such as in rice growing andhorticulture. How data necessary for prediction and analysis should be gathered can be a point of argument.
(2) Establishment of institutions for the use of AI and robotics
As introduced in the concrete case section, while the technology exists, it has not been widely
disseminated due to delays in the establishment of institutions. Therefore, it is necessary to establish therequired environment for the use of AI and robotics in the domain of agriculture. In 2017, the Ministry ofAgriculture, Forestry and Fisheries formulated the “Guidelines for ensuring safety of robotic equipment for
agriculture,” stipulating matters that should be respected by manufacturers and users to ensure safety, withconsideration of the practical realization of agricultural machines equipped with robot technology (robotic
Akihisa Kamata, “Fujitsu’s efforts toward agricultural ICT: To create agri-industry” (Growth Strategy Council -Investing for the
Future, Meeting for the “deepening of local Abenomics (agriculture) (5th meeting) (February 6, 2017) Material 3,” 2017.2.6, pp.56, 11. Office of Prime Minister Website<https://www.kantei.go.jp/jp/singi/keizaisaisei/miraitoshikaigi/suishinkaigo_dai5/siryou3.pdf> (in Japanese).
209 “Farmnote, launches ‘Farmnote Color,’ a wearable device for cattle using AI,” 2016.8.5. <https://farmnote.jp/pressrelease/farmnote-color2016.html> (in Japanese).
210 Farmnote Website <https://farmnote.jp/index.html> (in Japanese).
211 For example, “Farmnote Summit 2016: Be Connected,” 2016.11.16. Farmnote Website<https://farmnote.jp/events/summit2016.html> (in Japanese).
212 “Announcing the latest version of cattle management system ‘Farmnote 2.0,’ for ‘easy’ optimal management of ranch using
smart device,” 2015.4.13. Farmnote Website <https://farmnote.jp/press-release/20150413.html> (in Japanese). During theinterviews conducted by the author with users of Farmnote (on July 16, 2017), some noted that financial health is presented withnumerical values, and therefore, they can propose improvements even if they are not experienced.
213 Both the “Akisai” cloud for the food and agricultural industry mentioned earlier and “Farmnote” have a system that can beinstalled on smartphones so that users can view the status on farms and ranches.
208

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equipment for agriculture) that can drive autonomously or automatically perform tasks. 214 Under theStrategic Headquarters for the Promotion of an Advanced Information and Telecommunications Network
Society (IT Strategic Headquarters), guidelines are now being prepared regarding the standardization andhandling of agricultural information toward the proper use of agricultural big data.215(3) Adaptation to the needs of farmers and the environment
AI and robot technologies and devices for agriculture are now being studied and developed not only byagricultural industry personnel but also through collaboration between experts of leading-edge technology
in information science and engineering and private companies, including ventures. However, we need toensure that the performance of products developed based on engineering expertise matches the performance
demanded in actual agricultural workplaces. For example, there is a concept called “proper farming at proper
timing” in agriculture, and there are some crops that must be harvested all at once within a few days
depending on the weather. It is unacceptable if agricultural equipment that has not been used for several
months does not work during this period. We must note that agricultural equipment must properly function
and must be able to be fixed by users when malfunctions occur, even if this is not a cutting-edge technology
but an old technology that is a prerequisite for agricultural equipment.216
Arisa Ema, The University of Tokyo

Ⅷ Public Order and Security
1. Current situation concerning public order and security in Japan
In 2016, the number of police officials was 295,000. Of these, 7,797 were employees of the National
Police Agency, and 287,000 were employees of prefectural police departments. The number of recognizedcriminal offenses has been on the decline for 13 consecutive years since 2003. However, the number ofcrimes in which women and the elderly are victims, such as stalking and violence from spouses, and thenumber of crimes involving special fraud are very high. The increase in international terrorism and rise incybercrimes are also becoming problems.217
In terms of cybercrimes, the number of victims of illegal money transfers using internet banking isdecreasing, but “targeted email attacks,” which involve the theft of information of certain targetedorganizations or individuals, are on the rise. The number of arrested persons and number of arrests due to

Ministry of Agriculture, Forestry and Fisheries of Japan, “Guidelines for securing safety of autonomous driving of agriculturalequipment,” 2017.3. <http://www.maff.go.jp/j/kanbo/kihyo03/gityo/g_smart_nougyo/attach/pdf/index-2.pdf> (in Japanese).
215 “Strategic Headquarters for the Promotion of an Advanced Information and Telecommunications Network Society (IT Strategic
Headquarters) Report of expert panel on promotion of new strategies,” Office of Prime Minister Website<https://www.kantei.go.jp/jp/singi/it2/senmon_bunka/nougyou.html> (in Japanese).
216 Yohei Hoshino, “Efforts toward industry-agriculture cooperation: Damping control of boom sprayers and robot technology,”
Journal of the Robotics Society of Japan, Vol.35 No.5, 2017.6, p.392 (in Japanese).
217 National Police Agency (ed.), Police White Paper 2017, 2016, p.53.
< https://www.npa.go.jp/hakusyo/h28/english/WHITE_PAPER_ON_POLICE_2016/full_text_WHITE_PAPER_2016_.pdf >.
214

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unauthorized access are both the highest since the enactment of the Act Concerning the Prohibition of
Unauthorized Computer Access (Act No. 128 of 1999).218
The decrease in the number of those who take the police officer recruitment test due to the declining
birthrate and mass retirement in the field due to population aging are problems that Japan’s police
organizations will have to face over a long period of time. Police departments are actively involved inincreasing the number of local police officers and hiring and using retired police officers or female police
officers, but streamlining tasks is an issue.219 It is also estimated that there is a shortage of about 130,000
people handling information security as of 2016.220 Since the shortage of people in information security andthe lack of highly advanced skills among them are becoming issues,221 police organizations are promoting
personnel exchanges and information sharing with private businesses.222
2. Utilization of AI in maintaining and ensuring security
It is still difficult to secure human resources even though there have been various efforts. To ensureorder and security, various discussions have been held regarding the use of AI, robotics, and related
technologies under the perception that it is important not only to make arrests after the fact but also to prevent
crimes based on indications of crimes and to streamline security tasks. In addition to efforts toward the useof AI to identify and track crimes, it is important to made efforts to save labor and automate security tasks
and processes using drones and automated monitoring and detection systems and to perform “predictive
policing” in order to prevent crimes in advance.
(1) Identifying and tracking crimes
There are cases where criminals are identified and tracked using images gathered through surveillancecameras. For instance, the New York City Police Department has introduced a system for cross-checking
the images of criminals with images gathered through surveillance cameras and social media using a facialrecognition system.223 Republic of Korea (ROK) is also planning to introduce a system for detecting traffic
accidents and crimes in real time or for tracking suspects and cars using AI.224

National Police Agency, “About cyber threat landscape in 2016,” 2017.3.23, p.1.
<http://www.npa.go.jp/publications/statistics/cybersecurity/data/H28cyber_jousei.pdf>. (in Japanese).
219 Same as above, pp.198-199.
220 Information Services Industry Division, Commerce and Information Policy Bureau, Ministry of Economy, Trade and Industry,“Summary of the report: Results of the survey of the latest trend and future estimation of IT human resources,” 2016.6.10, p.12.
<http://www.meti.go.jp/policy/it_policy/jinzai/27FY/ITjinzai_report_summary.pdf>. (in Japanese).
221 National Police Agency Conference on Comprehensive Security Measures, “Human resources development for addressing
cybercrimes through government-industry collaboration: Report of the 2014 Conference on Comprehensive Security Measures,”2015.3, p.6. <https://www.npa.go.jp/cyber/csmeeting/h26/pdf/h26_honpen.pdf>. (in Japanese).
222 National Police Agency (ed.), op.cit. (217), pp.41-43. <https://www.npa.go.jp/hakusyo/h28/english/WHITE_PAPER_ON_POLICE_2016/full_text_WHITE_PAPER_2016_.pdf >.
223 Koji Hachiyama, “Current situations of efforts of crime prevention, security, and IT in US,” 2015.11, p.6.
<https://www.jetro.go.jp/ext_images/_Reports/02/cb0c9a0b7b7e9c15/rpNY_201511.pdf>. (in Japanese).
224 “Connecting with the police databases, Korea introduces “AI-equipped monitoring cameras in 2018,”
2017.6.5. Roboteer Website <https://roboteer-tokyo.com/archives/8979>. (in Japanese).
21885

It was reported by the media that the Durham Police in the UK will introduce a crime risk assessment
AI called “Harm Assessment Risk Tool (HART), which analyzes the severity of crimes and risks of suspects
and determines whether suspects need to be detained based on data regarding past crimes and the like.225(2) Security and investigations using drones
Drones are used to save labor and automate the implementation of security and related processes. At
the Ise-Shima Summit held in 2016, two types of drones were prepared: those for aerial photography andthose for preventing drone-based terrorism attacks. If suspicious objects flew by around the venue, the former
attempted to look for pilots flying drones from the air, while the latter aimed to capture suspicious objects
using a two-meter square net to ensure security.226
During an investigation of illegal dumping of waste materials, the Osaka Prefectural Police Department
significantly reduced its labor force by using drones to determine locations of illegal dumping inmountainous areas.227(3) Automated surveillance and detection system
To prevent terrorism and crimes in advance, a system is being developed to constantly monitor images
from surveillance cameras using a facial recognition system and to notify security guards via smartphonewhen persons on a blacklist are detected.228
In Japan, technology is being developed to detect abnormalities by having AI machine learn the staticstates of systems and monitor and analyze them in real time based on the data of operating states of PCs andservers (e.g., program booting, file access, communication) as a cyber security measure.229 Further, a systemfor detecting posted content and comments that may evolve into crimes based on open information posted
on social media and bulletin boards and for notifying the surveillants is provided to governmental agencies.230(4) Predictive policing
Predictive policing refers to efforts to predict crimes in advance by predicting the indications of crimes
based on big data and information from various sensors and reinforcing security from actual sites. There areadvanced cases of predictive policing using AI in the US, UK, Republic of Korea, and so forth.231“UK police introduces ‘crime risk evaluation AI,’ which automatically determines ‘whether to detain or not’ reducing false
accusation!?,” 2017.6.5. Same as above <https://roboteer-tokyo.com/archives/8986>. (in Japanese).
226 “Ise-Shima Summit 2016: Protect with the latest equipment, drones catching drones,” Mainichi Shimbun (Central region
version), 2016.5.23, p.7. (in Japanese).
227 “Drones now widely used for crime investigation and disaster rescue,” 2016.9.10. Sankei News Website<http://www.sankei.com/west/news/160910/wst1609100020-n1.html>. (in Japanese).
228 Isao Horikoshi, “Hopes for 5G from industry, even for prevention of crimes and unmanned driving,” 2016.11.25. ITPro Website<http://itpro.nikkeibp.co.jp/atcl/column/16/111400259/111400004/?rt=nocnt>. (in Japanese).
229 For example, “Cyber and physical: NEC’s unique security technology makes society more secure and safer,” 2017.3.10. NEC
wisdom Website <https://wisdom.nec.com/ja/events/2017031001/02.html>. (in Japanese).
230 UBIC, “UBIC discovers ‘indications of crimes’ on the internet, starts providing ‘Lit i View SNS MONITORING’ to ministries,”
2016.3.1. Fronteo Website <http://www.fronteo.com/corporate/news/uploadfile/docs/20160301.pdf >. (in Japanese).
231
Joel Gunter, “Chicago goes high-tech in search of answers to gun crime surge,” BBC News, 2017.6.19.
<http://www.bbc.com/news/world-us-canada-40293666>; Hachiyama, op.cit. (223), p.8; “Connecting with the police databases,
Korea introduces “AI-equipped monitoring cameras in 2018, 2017.6.5. Roboteer Website <https://roboteertokyo.com/archives/8979> (in Japanese).; “UK police introduces ‘crime risk evaluation AI,’ which automatically determines
225

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In Japan, the Kyoto Prefectural Police Department introduced the “Predictive Crime Defense System”(hereinafter “System”) for processing the past statistical materials accumulated using a prediction algorithm
based on multiple theories of criminology and predicting the time zones and locations where thefts and sex
crimes may occur using computers in October 2016. 232 However, the System did not adopt machinelearning; instead, humans created prediction logics to avoid turning analysis criteria into a “blackbox.”233
The prediction results are shared among devices used at Koban (police station) and police officers on duty
through a network extended even to the Koban (police station) level within the precinct, which has
contributed to streamlining police patrols.234 According to the person in charge of the System in the Kyoto
Prefectural Police Department, the System has contributed to dozens of arrests within a year of its
introduction.235
The System plays the function of allowing police officers with little experience or officers not familiar
with the area to perform police patrols smoothly. The System is also used for cooperation with crimeprevention volunteers in the area by releasing police patrol routes with detailed information abstracted, suchas concrete crime occurrence locations, based on the analysis results.236
3. Issues surrounding the informatization of security: Availability and credibility of data
Many crime prediction and detection technologies bear the issue of privacy and are strongly influenced
by the volume and quality of data. In the US, where many systems have been implemented, it is noted that
half of rapes, robberies, and heinous assaults are not reported and thus not registered in the databases, posing
questions regarding the credibility of such databases.237 Because the private companies that developed thesystems do not make analysis mechanisms available to the public (since they are company secrets), thecredibility of the analysis mechanisms has also been questioned.238 In Japan, where the absolute number ofrecognized criminal offenses is low in general, whether it is possible to improve the accuracy of theprediction results and expand the types of crimes that can be analyzed is a technological issue that should be
discussed further.239
In the Kyoto Prefectural Police Department, infrastructures, including accumulated data on crime
statistics and the network extended to the Koban (police station) level, were prepared in advance. Whether
other prefectural police departments can establish such infrastructure on their own when the budget is limited
and when there are various restrictions on the use of reasonably priced private-sector cloud services remains‘whether to detain or not’ reducing false accusation!?,” 2017.6.5. Same as above <https://roboteer-tokyo.com/archives/8986>. (inJapanese).
232 “Kyoto Prefectural Police Department: Introduces a crime prediction system in October first attempt in Japan,” Mainichi Shimbun(Osaka version), evening paper, p.9. (in Japanese).
233 Yohei Ichijima, “On-the-spot arrest with big data, Kyoto Prefectural Police Department’s prediction system,” Nikkei big data,
No.40, 2017.6, p.19. (in Japanese).
234 Interview with the person in charge at the Investigative Planning Division, Investigation Department, Kyoto Prefectural Police
Department (December 11, 2017).
235 Same as above.
236 Same as above.
237 Toshihiro Yamada, “Reasons why Kyoto Prefectural Police Department’s ‘crime prediction system’ is useful,” 2016.10.6.
ITmedia Website <http://www.itmedia.co.jp/business/articles/1610/06/news018.html>. (in Japanese).
238 Same as above.
239 Interview with the person in charge at the Investigative Planning Division, Investigation Department, Kyoto Prefectural Police
Department (December 11, 2017).

87one of the challenges for the dissemination of similar systems at a national scale. When adopting machinelearning, analysis criteria can turn into a blackbox. Therefore, the accountability of prediction results forexternal organizations and ways to convince police officers to accept them are other issues to be discussed.240
About the Introduction and Use of the “Predictive Crime Defense System” in the Kyoto Prefectural
Police Department(Summary of the interview)
Mr. Hiroaki Okamoto
Investigative Planning Division, Investigation Department, Kyoto Prefectural Police Department
1. Upon the introduction of the System
The Kyoto Prefectural Police Department actively deals with the application of new technologies topolicing and is working to address cybercrimes in a forward-thinking fashion. In terms of crime
prediction, the department visited the Santa Cruz Police Department and Los Angeles Police Departmentin the state of California, US, for inspection in 2015. Both of these departments have implemented acrime prediction system called “PredPol.” As a result of the inspection, the Kyoto Prefectural Police
Department realized that it was difficult to introduce the American system as it is because the number ofrecognized crimes in Japan is much smaller than that in the US and because there are differences in theurban structures and police organizations. Therefore, the Kyoto Prefectural Police Department decided
to build a unique system for Japan.
Data and infrastructure are necessary to build a system. Although police organizations in Japancannot use external cloud services, in about 2004, the Kyoto Prefectural Police Department began
accumulating crime data, established infrastructure for a unique geoinformation system (GIS), built asecure network that extends to the Koban (police station) level, and secured human resources familiar
with IT. The network is isolated from that of fire departments and hospitals, and there is no plan toconnect them as of today.
2. About the System
The system only accumulates data on the type of crime, date, time, and location. The types of crimes
predicted are property crimes such as a burglar and sex crimes. Manual profiling is carried out for crimes
with a small number of incident reports. The pattern of crime occurrence in the 150 square meter area isdisplayed. The department integrates (1) hot spot analysis theory and (2) theory of near repeat
victimization into the system algorithms, and it predicts future cases based on the past crime occurrence
data accumulated. The data are updated every few hours.

240

Same as above.

88

Figure 1. Screen of the System (sample)(Note) Colors indicate the types of crimes, and the density of colors indicate the degree of risk.
(Source) Investigative Planning Division, Investigation Department, Kyoto Prefectural Police Department

3. About the use of the System
Police officers can access the System from their PCs at the headquarters and Koban (police station)
through the secure network. The System is used when each police officer determines where to patrol orwhere to monitor, which contributes to efficient patrols. It also plays a supporting role so that even novice
police officers who do not have the experience or intuition of experienced police officers or police
officers who are not yet familiar with an area because they have just been assigned there can perform
patrols smoothly. It has been a year since the introduction of the System, and there have been dozens ofarrests through its use.
To “visualize” the achievements of the crime prediction system, the department shares use cases byhonoring the police officers who have used the data successfully, and it has established a mechanism for
motivating users.
Under the idea of community policing (cooperation between the police and the local community),
the community safety division discloses the patrol routes with detailed information abstracted, such as89

concrete crime occurrence locations, so that the System can be utilized in activities jointly carried out
with local crime prevention volunteers.
Figure 2. Patrol route for the community (sample)※ サンプル(Source) Investigative Planning Division, Investigation Department, Kyoto Prefectural Police Department

4. Future challenges
The System does not utilize deep learning. This is because it would be difficult for actual police
officers to be convinced of the mechanism as well as logic of judgements, and to follow prediction
results. It is necessary to evaluate the accuracy and credibility of the System itself in order to determine
whether the System is being used. It is also necessary to develop evaluation indexes for determining its
effect in deterring crime in the future.
The interview was conducted on December 11, 2017.
Takashi Seto, Asia Pacific Initiative
Arisa Ema, The University of Tokyo

Column 1: AI applications for Defense and National Security Overseas
1. Backgrounds on Applications of AI and robotics for defense and national security purposes
The use of unmanned weapon systems has increased in the military and national security spheres sincethe 2000s.241 For example, according to a survey conducted by the Congressional Research Service (CRS)
For example, see the following for the use of unmanned vehicles during the US’s anti-terrorism operations in the Middle Eastand North Africa. Jessica Purkiss and Jack Serle, “Obama's Covert Drone War in Numbers: Ten Times More Strikes than Bush,”

241

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in 2012, the share of drones among all the aircrafts owned by the US military was about 5% in 2005, but thisfigure drastically increased to about 40% in 2012.242
R&D investments in AI algorithm among the U.S. technology companies have continuously increased
to an estimated $20-$30 billion in 2016, and will grow to as high as 126 billion by 2025. 243 The
sophistication and proliferation of dual-use sensors embedded into military platforms allows militaryplanners access to big data from battlefield.244 In line with these trends, unique characteristics of AI such as
dual-use and omni-use technologies, leading to a growing interest in AI applications for defense and national
security, namely intelligence, surveillance, reconnaissance, logistics, cyberspace operations, command andcontrol, as well as autonomous245 weapon systems.246 According to some analysts, competing AI R&D
in relation to its use are now starting to be closely linked with military competition among nations.247
There are reasons why various countries have been aggressive in the application of AI and robotics for
defense and national security purposes. The first reason is that machines can replace humans in performing
3D duties. 248 In general, unmanned weapon systems (and even autonomous weapon systems) havetechnological characteristics, such as the sustainability of functions independent from the physiologicallimitations of humans, low risk for operators, ease of downsizing, and relatively low development andoperation costs compared with manned systems.249 These characteristics contribute to the streamlining ofoperations and reduction of operation costs through a reduction in labor costs, among others.250
Secondly, political and social factors surrounding advanced nations, primarily European countries andthe US, are sparking debate with respect to turning existing weapon systems into unmanned, labor-saving,
and autonomous systems. Examples of such factors include the shortage of military personnel due todeclining birthrates, aging population, and the popularization of higher education; prolonged and intensified
anti-terrorism operations; the increased risks of operations associated with Russia and China’s Anti-

2017.1.17. The Bureau of Investigate Journalism Website <https://www.thebureauinvestigates.com/stories/2017-01-17/obamascovert-drone-war-in-numbers-ten-times-more-strikes-than-bush>.
242 Jeremiah Gertler, “U.S. Unmanned Aerial Systems,” CRS Report for Congress, R42136, 2012.1.3, p.9.
<http://www.dtic.mil/dtic/tr/fulltext/u2/a566235.pdf>.
243 Danie S. Hoadley and Nathan J. Lucas, “Artificial Intelligence and National Security,” CRS Report for Congress, R45178, 2018
4.26,p.2
244 Linton Wells II, “National Security Implications of the Fourth Industrial Revolution,” 2016. 8.29, Centre of Excellence for
National Security (CENS), Distinguished Visitor Program (DVP) Lecture, STAR-TIDES Website, pp.7-15 <http://startides.net/sites/default/files/documents/files/Natnl%20Sec%20Impl%20of%204th%20Ind%20Rev%20for%20Singapore%20gene
ric%208-29-16b%20final.pdf>.
245 See the following for the difference between “unmanned” and “autonomous” and arguments over the definition of the latter.
Reito Kawaguchi, “A Consideration of the Progress of Future military Science and Technology and Military Management: About
Regulations on Lethal Autonomous Weapon Systems (LAWS), NIDS Journal of Defense and Security, Vol.19 No.1, 2016.12,
p.216. <http://www.nids.mod.go.jp/publication/kiyo/pdf/bulletin_j19_1_8.pdf> (in Japanese); See Hoadley and Lucas,
op.cit.(243) pp. 1-4.
246 Hoadley and Lucas, op.cit.(243) pp. 8-13, and Executive Summary.
247 Tom Simonite, “For Superpowers, Artificial Intelligence Fuels New Global Arms Race,” WIRED, 2017.9.8.
<https://www.wired.com/story/for-superpowers-artificial-intelligence-fuels-new-global-arms-race/>.
248 3D means dull, dirty, and dangerous. Typical duties include long-term monitoring, activities in areas contaminated withchemical agents, biological agents, or radiation, and search and removal of landmines and sea mines. Ken Jimbo, “Unmannedsystem, robotics and national security,” Kokusai Mondai (International Affairs), No.658, 2017.1.2, p.18 (in Japaese).
249 Ministry of Defense, “R&D vision for future unmanned equipment: Focusing on unmanned aircrafts,” 2016.8.31, p.3.
<http://www.mod.go.jp/atla/soubiseisaku/plan/vision/future_vision.pdf> (in Japanese).
250 Kawaguchi, op.cit.(245) p.217.

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Access/Area Denial (A2/AD)251 capabilities252; the national opinion in advanced democracies especiallyafter the end of the Cold War, leaning toward the direction that sacrificing own citizens in wars should be
avoided, which is a trend commonly known as a post-heroic society253, the escalating prices of defense
equipment and procurement,254 as well as budgetary constraints255 of the national defense ministries ofvarious countries.
In addition, some powers expect to maintain military superiority by AI applications and autonomous
weapons. The points of what experts argue are following; (1) autonomous weapon systems is likely to gain
greater battlefield advantages than manned ones, because they can be made smaller, lighter, faster, moremaneuverable, and resilient to electro-magnetic warfare being unbounded by physiological limits of humans.
(2) enhanced C4ISR (Command and Control Communication Computer Surveillance and Reconnaissance)and common operating picture (COP) through application of AI greatly streamlines multi-domain operations
across land, air, sea and outer-space as well as cyber-space, which maintains tactical superiority over andimposes significant costs on adversaries across multi-domain kinetic operations256.
2. Introduction of cases in various countries(1) United States of America
Under the administration of Barack Obama, the U.S. Defense Department (DoD) announced the“Third Offset Strategy257 (TOS)” as one of its strategic initiatives in November 2014.258 It is a strategy tokeep its technological edge and deterrence over adversarial peers, through pursuing a triple-pronged
approach of (1) technological innovation, (2) reinventing concept of operations (CONOPs) of U.S. Forces,and (3) organizational reform of the U.S. Defense Department and U.S. forces.259

A2/AD generally refers to a country’s ability to increase the risk of power-projection capabilities consisting of ships, aircrafts,
bases, and command and control systems and to be able to stop interventions with regional conflicts (environment) using ballistic
missile and cruise missile attacks, submarines, and cyber and electronic attacks. See the following for China and Russia’s A2/AD.
Stephen Biddle and Ivan Oelrich, “Future Warfare in the Western Pacific: Chinese Antiaccess/Area Denial, U.S. AirSea Battle,
and Command of the Commons in East Asia,” International Security, Vol.41 No.1, Summer 2016, pp.7-48.
<https://www.mitpressjournals.org/doi/pdf/10.1162/ISEC_a_00249>; Stephan Frühling and Guillaume Lasconjarias, “NATO,
A2/AD and the Kaliningrad Challenge,” Survival, Vol.58 No.2, 2016.3, pp.95-116.
252 See the following for a background on A2/AD ability and environments in which military has ask for unmanned weapons and
AI. Patrick M. Cronin et al., Dynamic Balance: An Alliance Requirements Roadmap for the Asia-Pacific Region, Washington:
Center for a New American Security, 2016.5, pp.11-12. <https://s3.amazonaws.com/files.cnas.org/documents/CNASReportAllianceRoadmap-Final.pdf?mtime=20161010171119>; Shawn Brimley et al., While We Can: Arresting the Erosion ofAmerica's Military Edge, idem, 2015.12, pp.5-9. <https://www.files.ethz.ch/isn/195455/While%20We%20Can-151207.pdf>.
253Marouf Hasian Jr., Drone Warfare and Lawfare in a Post-Heroic Age, Tuscaloosa: University of Alabama Press, 2016, pp.19-47.
254 Sam Jones, “AI and Robots Line up for Battlefield Service,” Financial Times, 2016.11.6.
<https://www.ft.com/content/02d4d586-78e9-11e6-97ae-647294649b28>.
255 Satoru Mori, “Chapter 5: ‘Offset Strategy’ and ‘national defense innovation initiative’ of US,” Japan Institute of International
Affairs (ed.), “Various domestic factors affecting US’s diplomatic policies,” Japan Institute of International Affairs, 2016.3, p.63.
<http://www2.jiia.or.jp/pdf/research/H27_US/05-mori.pdf> (in Japanese).
256 Paul Scharre, Army of None: Autonomous Weapons and the Future of War Newyork: W.W Norton ＆ Company, 2018 pp. 1416, Mori, op.cit.(255), pp.61-62; Satoru Mori, “Technology and national security: Vision for adoption of autonomy in US’s
national defense innovation,” Kokusai Mondai (International Affairs), No.658, 2017.1.2, pp.27-31 (in Japanese).
257 “Offset Strategy” is a national defense term in the US and means a “strategy to secure more than enough military capabilities tooffset military advantage of enemies by combining weapons, systems, and operation concepts in a new way to create deterrence.”
Mori, op.cit.(255), p.53 (in Japanese).
258 “Regan National Defense Forum Keynote: As Delivered by Secretary of Defense Chuck Hagel,” 2014.11.15. U.S. Departmentof Defense Website <https://www.defense.gov/News/Speeches/Speech-View/Article/606635/>; Secretary of Defense,
Memorandum: The Defense Innovation Initiative, 2014.11.15. <http://archive.defense.gov/pubs/OSD013411-14.pdf>.
259 Mori, “Technology and national security,” op.cit.(255), pp.55-56.
251

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DoD and the U.S. Forces have been struggling to keep military superiority over and deterrence throughinnovation and maintain their technological edge while cutting-edge technologies are quickly proliferating
throughout the world. Various measures of the strategy have been passed down to the current administrationof Donald Trump.260
In 2015, the Defense Science Board (DSB), an organization in charge of the promotion of TOS in the
U.S. Defense Department, discussed in detail the concept of “autonomy,” which means “autonomy” througha group of relevant technologies, such as AI, robotics, and big data, and the possibilities of their use fornational security purposes261. The DSB report includes recommendations on specific applications of AI in
the DoD, including swift information collection and tailored analysis, timely logistics based on monitoringof the status and condition of troops, and human-machine teaming through the integration of AI into various
layers of military platforms, C4ISR networks and operational process.262 The report also estimates that AI
can be utilized in the long run, for (1) predicting any destabilization in the political and socio-economic
landscape, (2) creating autonomous swarms,263 (3) detecting large-scale intrusions on the IoT systems, (4)building cyber-resilient weapons (equipped with the function to automatically fix the system throughmachine learning), and (5) performing swift planning of air operations.264 Following this, R&D as well as
AI experiments have been conducted within the Defense Advanced Research Projects Agency (DARPA)
and Intelligence Advanced Research Project Agency (IARPA), as well as among the 4 services. 265 . In
addition, the Office of the Assistant Secretary of Defense for Research and Engineering (ASD/RE) maintainloose oversight of these initiatives, and is in the process of drafting a DoD AI strategy, which said to be
published in summer 2018. 266 As of May 15 2018, some notable initiatives exist under Trump
Administration, accelerating the integration of AI into the DoD and U.S. Forces for planning and warfighting.

260Both National Security Strategy (NSS) and National Defense Strategy (NDS) formulated by the Trump administration also saythat research and development of advanced technology is important to secure the superiority of US, and AI is addressed as one of
the examples of advanced technology. National Security Strategy of the United States of America, 2017.12, pp.20-21.
<https://www.whitehouse.gov/wp-content/uploads/2017/12/NSS-Final-12-18-2017-0905.pdf>; Summary of the National Defense
Strategy Sharpening the American: Military’s Competitive Edge, 2018.1, pp.3, 7.
<https://www.defense.gov/Portals/1/Documents/pubs/2018-National-Defense-Strategy-Summary.pdf>. and also see Tom
Simonite, “Defense Secretary James Mattis Envies Silicon Valley's AI Ascent,” WIRED, 2017.8.11.
<https://www.wired.com/story/james-mattis-artificial-intelligence-diux/>.
261 The results of the deliberation were published as a report in the summer of 2016. See the following for the implications ofautonomy. Defense Science Board, Summer Study on Autonomy, 2016.6. <https://fas.org/irp/agency/dod/dsb/autonomy-ss.pdf>.
262 Ibid, pp.45-76..
263 A swarm means that a “swarm” of many drones share data collected by each drone in the swarm to perform attacks and defense
by synchronizing and coordinating the drones in the swarm as an autonomous weapon. By interconnecting the autonomous
drones, the swarm will gain invulnerability (ability to maintain functions) to electronic attacks, such as jamming. By effectively
using drones that can be mass produced, emerging countries and developing countries that tend to lose numerical (quantitative)
advantage in battlefields against the militaries of advanced countries, due to the costs of weapon manufacturing and people
development, will generally be able to offset their numerical disadvantage to enemies. Mori, “Technology and national security,”
op.cit. (255), p.30; Paul Scharre, Robotics on the Battlefield Part II: The Coming Swarm, Washington: Center for a New
American Security, 2014.10.
<https://s3.amazonaws.com/files.cnas.org/documents/CNAS_TheComingSwarm_Scharre.pdf?mtime=2016090608209>.
264 Defense Science Board, op.cit. (261), pp.76-97..
265 This includes the development of AI-equipped autonomous unmanned underwater vehicles (UUVs) and the structuring of strikegroups consisting of autonomous and manned vehicles. See the following for details. Shigeo Kikuchi & Hiromu Arakaki,“Chapter 7: US: Addressing the return of competition among major nations,” National Institute for Defense Studies (ed.), The
East Asian Strategic Review 2017, 2017, pp.197-202. <http://www.nids.mod.go.jp/publication/eastasian/pdf/eastasian2017/j07.pdf> (in Japanese).
266 Hoadley and Lucas, op.cit. (243) pp. 8-13.

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Firstly, DoD launched initiatives named Algorithmic Warfare Cross-Functional Team, known as“Project Maven” in April 2017.267 The purpose of this project is to accelerate, improve, and put to wider use
the military’s utilization of machine learning, especially for the time being, on intelligence collection andanalysis.268 As part of this project, machine learning has been utilized for the analysis of massive flows ofimages and videos from UAVs operating at Iraq and Syria, leading the hunt for insurgents of ISIL.269
Secondly, the U.S. Army Training and Doctrine Command (TRADOC) requested the reinforcement offunctions to gather and analyze information using AI in their report of the Multi-Domain Battle (MDB)
concept, which is a potential future CONOPs for the army, published in December 2017.270
Thirdly, under the incumbent Deputy Secretary of Defense Patrick Shanahan, the DoD is accelerating
initiatives for establishing infrastructures to boost the application of AI. They include military cloud data
storage covering multiple departments of the DoD and its services from decision makers to battlefield
operators, commonly known as Joint Enterprise Defense Infrastructure contract (JEDI)271, as well as the AI
Center of Excellence, which pull together multiple existing military programs regarding AI application andwork.272(2) Israel and Europe
Israel has been promoting the application of AI and robotics in 3D tasks of anti-terrorism operations
and border security in particular. In July 2016, Israel began fielding the AI-equipped, fully autonomous
military vehicle called “Border Protector,” which now fulfils the role of monitoring the border area in the
Gaza Strip in Palestinian territories. The vehicle is an unmanned ground vehicle (UGV) for surveillance andreconnaissance. At this point, it does not have the ability to kill or injure, but the Israeli military is planningto have it equipped with machine guns to build a combat troop teaming the autonomous weapon system andhumans soldiers and have it play a part in border protection.273 Analysts maintain that the revolving-door
systems between military, government and businesses is a feature of the Israeli AI start-up ecosystem274,
which leads to rapid application of AI to national security.
In Europe, NATO member states are also try to keep pace with the United States in the field of AI. In
March 2018, French President Emmanuel Macron announced a strategy for innovation and utilization of AI

Memorandum of Deputy Secretary of Defense, DoD, “Establishment of an Algorithmic Warfare Cross-Functional Team
(Project Maven),” < https://www.govexec.com/media/gbc/docs/pdfs_edit/establishment_of_the_awcft_project_maven.pdf>
2017.4.26.
268Sydney J. Freedberg Jr., “‘Algorithmic Warfare:’ DSD Work Unleashes AI on Intel Data,” 2017.4.28. Breaking Defense Website<https://breakingdefense.com/2017/04/dsd-work-unleashes-ai-on-intel-data-algorithmic-warfare/>.
269 Hoadley and Lucas, op.cit. (243) p.9, and Executive Summary.
270 United States Army Training and Doctrine Command, Multi-Domain Battle: Evolution of Combined Arms for the 21st Century:
2025-2040 (Version 1.0), 2017.12, p.54. <http://www.tradoc.army.mil/multidomainbattle/docs/MDB_Evolutionfor21st.pdf>.
271 Sydney J. Freedberg Jr. “War Cloud: JEDI To Deploy Backpack Servers To Front Line,” Breaking Defense Website, 2018. 4.
23 < https://breakingdefense.com/2018/04/war-cloud-jedi-to-deploy-backpack-servers-to-front-line/>
272 Matt Stroud R. and Jeffrey Smith, “Trump Administration Accelerates Military Study of Artificial Intelligence” The Center for
Public Integrity Website, 2018.4.26 < https://www.publicintegrity.org/2018/04/26/21707/trump-administration-acceleratesmilitary-study-artificial-intelligence>
273 “Endless war: Robot troop starts operation, autonomous military vehicles on actual battlefield, the world’s first attempt byIsrael,” Mainichi Shimbun, 2016.8.24, p.1 (in Japanese).
274 Shepherd Laughlin, “Spotlight: Israel’s Startup Ecosystem,” J. Walter Thompson Intelligence Website, 2017.6.2 <https://www.jwtintelligence.com/2017/06/spotlight-israels-startup-ecosystem/>
267

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in France and Europe, proposing the establishment of the DRAPA-like organization at the EU-level.275
NATO member states will conduct more than 20 AI-related experiments in next major exercise “Trident
Juncture”, organized by the Allied Command Transformation in October and November 2018, which include
an effort to autonomously detect, diagnose, and deliver care to wounded soldiers276.
(3) China & Russia
China and Russia are also trying to invest in research and development towards the aggressive use of
AI and robotics for military and national security purposes as national strategies, though China and Russia
are, for the time being, technologically inferior to the U.S.277 China’s “Next Generation AI Development
Plan” emphasizes that “China will finally seize the initiative” and reach “world leading levels” of AI
investment by 2030, spending over $150 billion on R&D through government funding.278 Russia, as well,has been struggling to keep pace with the U.S. and China. The Russian Military Industrial Committee, forexample, has set a goal for 30% of military equipment to be replaced by robotics by 2025, and the Russian
government has established a DARPA-like organization as well as initiated an annual conference on“Robotization of the Russian Federation”.279
Their approach of technological application have been developing with those of the U.S. in mind.
Simply put, they are also trying to upgrade intelligence collection and analysis, cyber-defense readiness,
identification and tracking of targets as well as precision-strike capabilities, and command and control for
their armed forces, even though their ethics and limits on military use of AI may be different from those of
the U.S.280
3. Future challenges of AI applications to defense and national security(1) Debates and issues pertaining to regulations over lethal autonomous weapon systems (LAWS)
Scientists and NGOs of various countries have insisted on regulations over the development of LAWS,
which can autonomously target and kill/injure targets without the supervision of human operators, commonly
called lethal autonomous weapon systems with “humans out of the loop”.281 Regulations over LAWS are

Government of Republic of France, Press Release, “Artificial Intelligence: ‘Making France a leader’,” 2018.3.18. <https://www.gouvernement.fr/en/artificial-intelligence-making-france-a-leader>
276 Patrick Tucker, “How NATO’s Transformation Chief Is Pushing the Alliance to Keep Up in AI,” Defense One Website,
2018.5.18, < https://www.defenseone.com/technology/2018/05/how-natos-transformation-chief-pushing-alliance-keepai/148301/>
277 Simonite, op.cit.(260) Stephan De Spiegeleire et al., Artificial Intelligence and the Future of Defense: Strategic Implicationsfor Small- and Medium-Sized Force Providers, Hague: The Hague Centre for Strategic Studies, 2017, pp.77-84.
<https://hcss.nl/sites/default/files/files/reports/Artificial%20Intelligence%20and%20the%20Future%20of%20Defense.pdf>.
278 Hoadley and Lucas, op.cit. (243), pp.17-18, On overview of China’s AI development strategy and PLA military thinking on AI
and warfare, see following Elsa B. Kania, Battlefield Singularity: Artificial Intelligence, Military Revolutions, and China’s Future
Military Power, Washington: Center for New American Security, 2017. 11.28, pp. 8-19. <https://s3.amazonaws.com/files.cnas.org/documents/Battlefield-Singularity-November-2017.pdf?mtime=20171129235804 >279 Hoadley and Lucas, Hoadley and Lucas, op.cit. (243) pp. 21-22, see also following Samuel Bendett, “Robots Rising: Behind the
Rapid Development of Russian Unmanned Military Systems,” Strategy Bridge, 2017. 12. 12.
<https://www.realcleardefense.com/articles/2017/12/12/red_robots_risin_112770.html>
280 Hoadley and Lucas, Hoadley and Lucas, op.cit. (243) pp. 21-22.
281 For example, see、Human Rights Watch and Harvard Laws School's International Human Rights Clinic, “Losing Humanity:
The Case against Killer Robots,” 2012.11.
<https://www.hrw.org/sites/default/files/reports/Losing%20Humanity%20Executive%20Summary.pdf>; Samuel Gibbs, “ElonMusk leads 116 experts calling for outright ban of killer robots,” Guardian, 2017.8.20.
275

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now being discussed mainly within the framework of the Convention on Certain Conventional Weapons(CCW) 282 from the perspectives of the international humanitarian law and international arms control
regimes.283 On the other hand, debates and articulation on regulatory schemes of weapons that do not exist
at this point are difficult. Some experts are concerned that early and strict regulations may interfere with
R&D efforts in the private sector, considering the dual use nature of AI, and may have negative impacts on
the development and application of other useful military AI technologies.284(2) Influence on international relations and national security policies
Experts of international relations and national security policies in the U.S. and Europe emphasize that
AI will influence wars and international relations as much as nuclear weapons did in the past.285
Experts point out that the features and consequences of AI applications, namely the rapid proliferationof underlying technologies, the growing accuracy and speed of intelligence, and subsequent rapid decisionmaking and operational tempo, may lead to reduce “strategic stability286” among great powers in three ways:(1) accelerating an arms race, (2) increasing the risk of accidental escalation during crisis and conflict, and(3) degrading the stability of nuclear deterrence among nuclear states through their fear of their own secondstrike capabilities being devastated by the adversaries’ rapid and accurate first-strike augmented by AI, which
would incentivize them to rush into their nuclear first-use.287
Furthermore, some are concerned that an overwhelming disparity regarding their tactical and operational
level of C4ISR between allies, with one that has integrated AI into their platforms compared to the other thathas not, may negatively impact joint operations under contingency.288(3) Challenges on organizational culture and acquisition process, and data accessibility / security of defense
and national security organizations<https://www.theguardian.com/technology/2017/aug/20/elon-musk-killer-robots-experts-outright-ban-lethal-autonomousweapons-war>.
282 Convention on Certain Conventional Weapons (Ordinance No. 12 of 1983) (enacted on December January 1083 and came into
effect on the same day in Japan).
283 See Kawaguchi, op.cit.(245), pp.221-223, for the trend of past discussions. CCW’s LAWS experts meeting was held from
November 13 to 18 in 2017. The delegates of the Japanese government stated, “it is difficult to gain a consensus on the clear
definition of LAWS at this moment, but it is important to have realistic discussions on the current situations of technologies thathave autonomous functions and their future prospect. Japan would like to participate in discussions actively, for example, by
providing knowledge, among others, as a country that owns advanced technology in terms of robotics and AI in relation to
LAWS. “About the meeting of the CCW's Group of Governmental Experts on LAWS,” 2017.11.20. Ministry of Foreign Affairsof Japan Website <http://www.mofa.go.jp/mofaj/press/release/press4_005290.html> (in Japanese).
284 Mary L. Cummings, Artificial Intelligence and the Future of Warfare, London: Chatham House, 2017.1, pp.1-12.
<https://www.chathamhouse.org/sites/files/chathamhouse/publications/research/2017-01-26-artificial-intelligence-future-warfarecummings-final.pdf>, Heigo Sato, “Warfare of Lethal Autonomous Weapon Systems,” Takashi Kawakami eds, What is future of
Warfare, Minerva Publications, 2017.1.20 p. 68 (in Japanese).
285 For example, see the following. Greg Allen and Taniel Chan, Artificial Intelligence and National Security, Cambridge: Harvard
Kennedy School Belfer Center for Science and International Affairs, 2017.7, pp.9-10.
<https://www.belfercenter.org/sites/default/files/files/publication/AI%20NatSec%20-%20final.pdf>
286 Strategic stability is a technical term in the field of nuclear deterrence, which can be divided into the issues of incentives on (1)nuclear first-use, (2) conflict escalation, and (3) arms-control(race). See following Edward Geist and Andrew J. Lohn, How Might
Artificial Intelligence Affect the Risk of Nuclear War? RAND Cooperation 2018.4.23 p.8.
https://www.rand.org/pubs/perspectives/PE296.html > and Jürgen Altmann and Frank Sauer, “Autonomous Weapon Systems
and Strategic Stability,” Survival, Vol.59 No.5, 2017.10/11, pp.121-122.
287 Geist and Lohn, Ibid. pp. 6-20. On AI arms race, see following Altmann and Sauer, Ibid.pp.120-135.
288 Mori, “Technology and national security,” op.cit. (255), pp.32-33.

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The organizational culture of military and national defense authorities can be factors that hinder the useof AI and autonomy in the military and national security spheres. Militaries have a strong tendency to value
hierarchy and top-down command and control. If decisions are made autonomously or automatically(decentralization) and if organizations become flat with the introduction of AI289 , this may cause somefriction between technology and the existing organizational culture. 290 In addition, some militaryorganizations traditionally have a strong tendency to emphasize manpower and other human elements. In
U.S. military forces where unmanned weapon systems have been implemented at the highest standard, many
people revolted against implementing unmanned labor-saving systems during the process of procuring andoperating unmanned attack planes such as the X-47. 291 Such an organizational culture can be a factor
hindering the dissemination of AI and autonomous weapons in the military and national security spheres.
It has also been noted that there may be conflicts between the organizational cultures of national security
organizations and private sector including tech-giants, start-ups and universities.292 Exposure of Google’scontribution to the DoD’s Project Maven in April 2018, for example, has sparked fierce protest and debate
among their engineers on their involvement in defense programs.293 The other pressing issue is the reformof the acquisition process for AI because there has been huge gap between the lingering process of defense
acquisition (in the case of US, 91 months for defining requirement, on average) and private software vendors
production cycle (around 6 to 9 months)294.
Finally, accessibility and security of data sources for military AI pose challenges. Military informationand intelligence is often classified, protected and stove-piped among services, having themselves difficult tobe proceeded for machine learning 295 . In addition, complex and constantly changing environment of
battlefield could easily degrade credibility of data pool, and allows adversaries’ data poisoning, leading toflaw and vulnerabilities of learning process296.
Takashi Seto, Asia Pacific Initiative289

See following discussion of Peter W. Singer, Wired for War: The Robotics Revolution and Conflicts in the 21st Century, Penguin
Books 2009.
290 Lena Andrews and Julia Macdonald, “Five Costs of Military Innovation,” 2016.2.18. War on the Rocks Website<https://warontherocks.com/2016/02/five-costs-of-military-innovation/>; Cummings, op.cit.(284), p.9; Denise E. Zheng andWilliam A. Carter, Leveraging the Internet of Things for a More Efficient and Effective Military, Washington: Center for Strategic
and International Studies, 2015.9, p.23. <https://csis-prod.s3.amazonaws.com/s3fspublic/legacy_files/files/publication/150915_Zheng_LeveragingInternet_WEB.pdf>
291 Cummings, ibid. It is said that there is a clear hierarchy between pilots of manned aircrafts and operators of unmanned aircrafts
in the air force, where duties of pilots are considered particularly important.
Koji Inoue, Century of drones: From aerial photography, parcel delivery, to armed unmanned aircrafts, Chuokoron Shinsha,
2015, p.176 (in Japanese).
292 Cummings, op.cit. (284), pp.8-12.
293 Sean Holliste, “Project Maven: Nearly a Dozen Google Employees Have Reportedly Quit in Protest,” CNET Website <com/news/google-project-maven-drone-protect-resign/> 2018.5.14.
294 Hoadley and Lucas, op.cit. (243) pp. 13-17.
295 Hoadley and Lucas, op.cit. (243). pp.7-8.
296 Greg Allen and Taniel Chan, op.cit. (285), p.25. Hoadley and Lucas, op.cit. (243), pp. 31-33.

97Column 2: Japanese Chess (Shōgi)
1. Significance of implementing software in the world of shōgi
In the Shōgi DENOU Tournament,297 professional shōgi players lost to shōgi software (hereinafter“software”). The news drew considerable attention within Japanese society.298 People began to realize thatvarious professions would be replaced by software due to advancements in AI technology, and some showed
concern on shogi-related websites that the professional status of shōgi players is at risk, as they were
overwhelmed by software during the games.299
However, we feel that the concern regarding “AI taking away shōgi players’ jobs” is based on mere
speculation without any good justification. Professional shōgi players have a profession not because theyplay shōgi but because shōgi fans, newspapers, and TV stations pay to watch the games, see the game records,
and hear the related commentary. Certainly, the Shōgi DENOU Tournament, where shōgi players andsoftware played games, achieved a certain level of success as a form of entertainment.300 However, thegames in the World Computer Shōgi Championship, where software plays against software, and“floodgate,” 301 the site for computer shōgi games, are not necessarily attracting more fans than shōgi
tournaments where professional shōgi players play against each other. The fee-charging shōgi gamebroadcasting app (“Japan Shōgi Association’s Mobile Broadcasting”) mainly broadcasts games between
shōgi players, whereas not many fans pay for records of or commentaries on games played by computers, as
shown by the fact that anyone can access floodgate for free. It is also difficult to implement elements otherthan “being good at shōgi”, which are the foundations underlying the profession of shōgi players (e.g., beinga good commentator or teacher, having personal magnetism, interacting with fans), into the software.
Currently, shōgi software has not achieved anything when it comes to “replacing the profession of shōgi
players,” and it has no prospect for doing so.
In terms of the introduction of software in the world of shōgi, instead of software’s potential for
replacing human jobs, we need to look into how software and humans can collaborate with each other andhow human actions change through collaboration with software, when software obtains abilities equal orsuperior to the level of people, as discussed later.

The DENOU Tournament is a shōgi tournament sponsored by Dwango since 2012 where shōgi players and shōgi software playeach other. “DENOU Tournament” Japan Shōgi Association Website <https://www.shogi.or.jp/match/denou/>. (in Japanese)
298 “Professional shōgi players are defeated in a row, 3rd game of the second DENOU Tournament, ‘Tsutsukana’ goes on theoffensive from the beginning, ‘It was strong’,” Asahi Shimbun, 2013.4.9, p.7 (in Japanese); “Last game of DENOU Tournament,
software wins, Grand shōgi master Sato, ‘moves I have never thought of’,” Mainichi Shimbun, 2017.5.21, p.30. (in Japanese)
299 “Records of internet flaming,” 2016.9.11 A-class League Move No.1 (Blog of Mr. Hidenori Ito, developer of shōgi software)<http://aleag.cocolog-nifty.com/blog/2016/09/post-e24a.html> (in Japanese).
300 During the second DENOU Tournament in 2013 and the third DENOU Tournament in 2014, it is said that the total number
viewers of “Niconico Namahoso,” a live streaming service provided by Dwango, exceeded 2 million. “Number of those who
watched DENOU Tournament on Niconico Namahoso exceeds 2 million, hitting a record high of shōgi-related broadcasting
programs,” 2013.4.22. MyNavi News <https://news.mynavi.jp/article/20130422-a287/> (in Japanese); The total number ofviewers of the third Shōgi DENOU Tournament reaches 2.13 million, exceeding that of previous year. 710,000 people watched
the fifth game, hitting a record high,” 2014.4.15. MyNavi News <https://news.mynavi.jp/article/20140415-a443/> (in Japanese).
301 Computer Shōgi-Server (floodgate) <http://wdoor.c.u-tokyo.ac.jp/shogi/floodgate.html>. (in Japanese)297

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2. Shifts in the development of shōgi software
The development of shōgi software began in the 1970s. Up to the early 2000s, evaluation functions302
were built manually based mainly on the knowledge of developers familiar with shōgi and advice fromprofessional shōgi players. What was considered important was to understand how top-class human shōgi
players choose a move in their head and to reproduce such thought processes in software using algorisms.303
In contrast, the method of Bonanza, software developed in 2005 by Mr. Kunihito Hoki (currently
associate professor of the University of Electro-Communications, Graduate School of Informatics andEngineering, School of Informatics and Engineering), who was a physical chemist and was not very familiar
with shōgi at the time, is based on a full width search, which evaluates all the moves from every aspect, andon records of games played by professional shōgi players. Major software using this method was
subsequently developed. They aimed to make software change (learn) to choose the moves that are likely to
be chosen by top-class shōgi players with a high probability, instead of converting shōgi moves into explicit
sets of rules (algorithm). Therefore, there has been a setback in terms of the elucidation of human thoughtprocesses during shōgi games.304 Since the speed and accuracy of learning improve as computers become
faster, even developers themselves cannot easily understand how major software chooses the moves.
Thus, the shōgi software developed recently that ended up overwhelming shōgi players fully utilized
the computing skills of computers, highly accurate reading abilities (search of aspects that can be anticipated),
and taikyokukan (broad-based perspective) (evaluation functions), but thought processes leading to a move
have gradually turned into a blackbox. We cannot make generalizations without careful consideration, but
process where the internal principle of software is difficult to understand based on the intellectual abilitiesof people, including the developers themselves, as the capabilities of software are enhanced (or as theelaboration of internal mechanisms independent from the intellectual understanding of people results in theenhancement of software capabilities) may be commonly seen among the core technologies of the recent AI
boom, including machine learning and deep learning.
3. Influence of shōgi software
Shōgi players finished the Shōgi DENOU Tournament, with 5 wins, 10 losses, and 1 draw. In responseto these results and the game content,305 shōgi software began to have various effects on professional shōgi
players. The unusual moves used by software in the Shōgi DENOU Tournament began to be used during
games between professional shōgi players. The effectiveness of such moves is now explored as the “newmoves of software.” Some of these new moves are mentioned in books regarding jōseki (recommended
sequences of moves). Thus, the influence of software is apparent in some jōseki and tactics. This influenceof software is an extension of choices through the “search of information.” Software equipped with the “fullwidth search” function, which can search all possible moves for every aspect, demonstrated that there are

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Functions that output values representing the advantage and disadvantage of aspects. These are the main elements that affectbinary search, discussed later, and software’s ability related to shōgi.
303 Hiroyuki Iida, Can computer win against grand shōgi master?, Iwanami Shoten, 2002. (in Japanese)
304 Computer Shōgi Association (ed.), How to create computer shōgi that can win against humans, Gijutsu-Hyohron Co., 2012. (inJapanese)305 Akinori Kubo, “How we should deal with computing intellect: Near future of humans and computers seen from Shōgi DENOU
Tournament,” 2014.3.29. SYNODOS Website <http://synodos.jp/science/7549>. (in Japanese)99

possibilities for moves that were likely to be excluded in taikyokukan, which professional shōgi players havedeveloped.
Now, professional shōgi players, mostly young ones, have begun to use software for research before
the final phase of the Shōgi DENOU Tournament and even after the tournament. Here, they are not just
exploring the possibilities of new moves and tactics but also trying to change their taikyokukan by referencing
the positional judgments of software, thinking that taikyokukan, which has been considered sacred as theessence of shōgi players’ ability, might just be “prejudice,” which is based on conventional ideas andemotions such as fear and hope.306 These trends are very suggestive of the way that software is beginning toassume an important role not just for the “search of information” but for the “evaluation of information,”
which only humans have been doing thus far. Shifts from tools for supporting the “search of information” totools for also supporting the “evaluation of information” can be amplified to shifts from “internet-related”
technology to technology now called “AI” in more general terms.
4. Future prospects
Yet, there are only a few professional shōgi players who have an entirely positive opinion about researchusing software (including consideration of the evaluation value). If the evaluation value of software is notbad, players can choose the moves that have been traditionally avoided because of the lower odds of winningand their fear of using them during actual games. In this sense, software has become a “lucky charm” (aphase used by a young shōgi player) that eases the players’ anxiety when they choose moves that havetraditionally been avoided, clearly expanding the possibilities of those shōgi players.
On the other hand, some players have expressed the negative opinion that if they rely too much on theevaluation value of software, they will tend not to think with their head and thus “lose their comprehension
muscle.”307 As discussed earlier, the thought processes involved in the major software currently available
have become too complex for people to understand. If they follow only the surface of the choices proposedby software, they may lose control of the progress of the game, which may lead to the deterioration of theirthought processes and the use of ill-supported moves.
The changes in today’s shōgi games as a result of software, as described above, were rapidly facilitatedin various ways, because the success or failure of shōgi as a form of entertainment in allowing people towatch “games” is considered the most important element in the shōgi profession and there are relatively
fewer customary or ethical constraints on them. However, we cannot easily view the introduction of softwarein the world of shōgi as an example of labor market changes as a result of AI, as discussed at the beginning.
We can consider this a pioneering model case that is highly suggestive even for other domains, from theperspective of how collaboration with software equipped with highly intellectual abilities can change ourthoughts and behaviors.
Akinori Kubo, Hitotsubashi University

Shota Chida & Akinori Kubo, “Machine and human, going the third way,” E! Eureka Project, Vol.8, 2016.4.20, pp.10-42.
<http://eureka-project.jp/2016/04/20/e8/#>. (in Japanese)
307 Shintaro Okawa, Indomitable shōgi player, Kodansha, 2016, pp.196-203. (in Japanese)
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