Tentative
Translation

The Conference toward AI Network Society
The Committee on AI Economy
2020 Report (Outline)

July 2020

Chapter 1 Background (History from the establishment of “the Committee on AI
Economy” to the present)

1

Establishment of “the Committee on AI Economy”
 It was established with the aim of considering what kind of social economy should be pursued and how the basic
policy and medium-to-long term strategies should be through the promotion of the implementation of AI insociety in January 2019.
Outline of “The Committee on AI Economy Report Outline in the Conference toward AI Network Society” (May2019)
 In the report published in May 2019, the directivity was pointed out in terms of the following 5 key points.
(1) The directivity to be pursued in AI economy (Realization of ”Inclusive AI Economy Society")(2) Ideal AI investment for sustainable economic growth and the improvement of productivity(3) How the industrial infrastructure that supports AI economy (Labor/R&D) should be(4) Japanese enterprises’ global competitiveness over the utilization of AI(5) Ideal basic policies and strategies on AI economy
The scope of consideration in this report”The Committee on AI Economy” that resumed its consideration in December 2019 pointed out the following 3 items as items to be further considered.
In addition, the Special-Interest Group on Data was established with the aim of considering professional/technical items on data economy policy.

(1) Consideration on how the utilization of data requested for the implementation of AI in society
We will consider how the policies should be for the promotion of the utilization of AI/data by grasping how the data should be
utilized on the premise that all entities are involved in the implementation of AI in society.
(2) Consideration on the data economy policy in the AI era
In the report published in May 2019, we positioned data as one of production factors, and showed how significant it is to estimate
its impact on the improvement of productivity and the necessity of deepening the discussion on a fair remuneration about data. We
will consider the estimation method of the value of data and how fair remuneration should be depending on the effects/value ofdata in order to launch these items.
(3) Consideration on the image of ”Inclusive AI Economy Society"We look at the future image of the ”Inclusive AI Economy Society“ that can be reached after overcoming the issues shown in (1)and (2).

Chapter 2 The ideal way to utilize data required for the Implementation of AI in Society

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The current status of the utilization of data in Japan The global economy has changed from a finance-led economy to the one led by digital companies that utilize ICT anddata. Japan’s competitiveness in the field of digital ICT has been stagnant.
 Regarding the utilization of data in enterprises, there are big disparities between large-scale enterprises and SMEs,
and enterprises in large city areas and ones in rural areas.
 Many Japanese enterprises realize that their initiatives for the utilization of data including data collection and analysis
are lagging behind because of the lack of human resources for the utilization of data, issues of expenses for theutilization of data and the lack of data collection/storage, even though they are eager to utilize data.
 Many Japanese companies consider that utilizing data in their company was effective.
 Consumers are careful about providing their personal information, behavior history, etc.
Based on the current status on the utilization of data in Japan, consider theideal way to utilize data required for the implementation of AI in society

The ideal way to utilize data required for the implementation of AI in society In enterprises, ①Position data/AI in management strategies and business architectures, ②Build physical datacollection system based on the utilization of data, organizational frameworks for the utilization of data anddata infrastructure in organizations, and ③Eliminate or alleviate the anxiety of consumers who provide
information as well as respond to the current data protection regulation for customers.
 In administration, accelerate the initiatives to become a “digital government.” It’s important to promote
opportunities to utilize public data such as open data catalog sites, DATA.GO.JP and e-Stat.
 Through the COVID-19 pandemic, the importance of communication network and ICT tools was realized, and it
is expected that the awareness of enterprises and individuals will be transformed, going forward. In order tofully exert the potential of communication network and ICT tools, it is essential to realize the ideal way toutilize data.

Chapter 3 Data Economy Policy in the AI era

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The functions/roles of data as a "new property" and estimation methods of the effects/value of data

 It’s difficult to define or describe the value of data uniquely, because data have various functions/roles andcharacteristics. Data (or data-related things). The approaches of estimation methods of the value of data can beroughly divided into three categories.
Table 1: Estimation Methods of the value of data

Approaches

Methods

Tools

Limitations

IssuesCost-based
Approach• Calculation method whichpays attention to costs
related to the creation,
management, andutilization of data
(personnel, and time, etc.)
It is based on theassumption that the valueof data is worth the cost.

• Calculating personnel
expenses• Calculating time
costs
• Calculating security
measure costs• This approach can’t take into accountquality and productivity.
• Since prices or quality of ICT
equipment are diverse, the estimatedvalue of data may be affected by otherfactors.
• It is difficult to intuitively envision a
relationship between cost and value.

• Which aspect of cost can beappropriately ascertained inorder to define the value ofdata?

Market-based
Approach• Calculation method which
is based on the market
prices of similar products
or of willingness of users
to pay for those products• Market prices (datatransactions, etc.)• Representationpreference method• Case studies of M&A,
negative effects• Experimental works• Lack of data for estimates• In the case of stated preference
method, the result can be affected by
the user's bias.
• The value could be perceived
differently between the business sideand the consumer side.

• How is data in general measured(not individual data)?
• Which point of view is more
appropriate for datameasurement, the business sideor the consumer side?

Impact-based
Approach• Calculation method where
the effects (productivity,
and revenue, etc.) of
utilizing data is estimated• Econometric analysis(production function
analysis,
andregression analysis,etc.)• Experimental works• Statistical estimates are needed.
• It is difficult to differentiate between
the effects of one type of data and theeffects of other types of data.

• It is necessary to clarify subjects
for which the value of data is tobe estimated (the definition andthe value of data)• How can a good analysis model
be established based on
consideration of complementaryelements which are necessary
for data to create value?

Source：Prepared based on a published document, etc.

Chapter 3 Data Economy Policy in the AI era

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The functions/roles of data as a "new property" and estimation methods of the effects/value of data The study conducted empirical analysis with the Production Function that is an impact-based approach as afeasible method to estimate the value of data, noting organizing the functions/roles of data and general
purposes/reproductivities applicable to industries and nations, etc.
 As a result, it turned out that the utilized data amount/number has a positive relationship with the added value aswell as other production factors (capital and labor).
 Regarding the above mentioned, the status of utilizing data in the following actual business scenes is assumed asfollows.
※Please note that this is just an “assumption”, and it doesn’t show each company’s initiative learned through ahearing survey, etc.
● ① Manufacturing: The companies that grasp the operational status of facilities and detect the abnormality offacilities tend to have realized stable production by visualizing sensor data, etc.
② Retailing: The sales of the companies that finely classify the types of customers from customer attributes andpast purchase data, etc. and take optimum marketing measures tend to have increased.
③ EC sites: The sales of EC sites that recommend optimum products for customers based on their customers’favors and past behavior pattern data tend to have increased.
Table 2: Image of estimation of the value of data through empirical analysis
Analyze the relationship between the owning/utilization of data and output.
As a result, it turned out that the utilized data has a positive relationship with added value.

CapitalAdded Value(Fixed assets＋
Intangible fixed
assets)Labor(Total number of
employees)

Data variable

Others(Differences inindustries)

Chapter 3 Data Economy Policy in the AI era

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The functions/roles of data as a "new property" and estimation methods of the effects/value of dataThe study positioned data as a production factor, as with other production factors (capital and labor), and conducted empirical analysis with the CobbDouglas Production Function modified to include data as a factor (linear homogeneity was not assumed).
log 𝑉𝑉 = log 𝐴𝐴𝑜𝑜 + 𝛼𝛼 log 𝐾𝐾 + 𝛽𝛽 log 𝐿𝐿 + 𝛾𝛾 log 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 + 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷*
𝑉𝑉 = 𝐴𝐴𝑜𝑜 𝐾𝐾 𝛼𝛼 𝐿𝐿𝛽𝛽 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝛾𝛾 ,*Classified into manufacturing and non-manufacturing

Results from an empirical analysis showed that the utilized data amount/number has a positive relationship with the added value as well asother production factors (capital and labor), and the added value increased by 0.05% when the utilized data amount/number increased by 1%.
However, it should be noted that this reflects the current status of initiatives of the utilization of data, and that increasing the utilized dataamount/number does not necessarily lead to an increase in added value, and that the results show the average effects between enterprises
who were successful by utilizing data and those who were not.
Table 3: Analysis Results

Data variable
Utilized data amount (= total data amount x ratio of data used for analysis)
Utilized data number (= total number of data x ratio of data used for analysis)
Utilized data amount (FY2015)
Utilized data number (FY2015)
Externally acquired data amount (= total data amount x ratio of data acquired
from outside)
Externally acquired data number (= total number of data x ratio of data acquired
from outside)
Internally owned data amount (= total data amount - externally acquired dataamount)Internally owned data number (= total number of data - externally acquired data
number)Total data amount x data utilization rate (use area/type/processing method)
Total number of data x data utilization rate
Total data amount x data diversity (diversity of data acquisition and provision)
Total number of data x data diversity

Samplesize

Adj R2

258
135
258
135
267

0.83430.81570.83320.81360.8401140

0.8193267

0.83791400.8134

261
137
173
85

0.83340.81500.85370.8576※Variables not described as FY2015 are values for FY2018.
The ratio of externally obtained data was corrected based on the situation of external data acquisition.

K(Capital)
0.44 ◎
0.34 ◎
0.44 ◎
0.34 ◎

L(Labor)0.50 ◎
0.55 ◎
0.51 ◎
0.56 ◎

Data(Data)
0.05 ◎
0.07 ◎
0.05 ◎
0.06 ○

0.47 ◎

0.47 ◎

0.05 ◎

0.35 ◎

0.51 ◎

0.07 ◎

0.47 ◎

0.48 ◎

0.05 ○

0.35 ◎

0.55 ◎

0.06 ○

0.44 ◎
0.32 ◎
0.53 ◎
0.41 ◎

0.49 ◎
0.58 ◎
0.42 ◎
0.49 ◎

0.05 ◎
0.05 ○
0.04 ○
0.06 ◎(Note) ◎：significance level 1％、○： 5％、 △： 10％

Chapter 3 Data Economy Policy in the AI era

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The ideal fair remuneration according to the effects/value of data
 In many cases, data are treated as capital, but there are some opinions that data should be treated as labor and individuals who
can produce data should be rewarded financially.
 As for paying remuneration for the users who produce data, while it’s positively seen from the standpoint of “data as labor”, it’sseen both positively and negatively from the standpoint of “data as capital.” There are some expected issues in both standpoints.
Table 4: Major characteristics of “Data as Capital” and “Data as Labor”

Data as Capital

Data as Labor

Contributors
to creating
the value of
data

Value utilizers of data

Incentive

Entrepreneurialism

Value the creators of
data
Usual contribution(equal to labor)

Future of
Labor

Universal Basic Income

Expected
concerns

・AI’s contribution to・Tragedy of the
improving productivity anticommonsmight be hindered(possibility that assets
because AI depends on to be shared are
active involvement of
subdivided andindividuals who produce privatized, thenrelated data.
utilization of resources
useful in society can be・Market dynamism
might become stagnant hindered) might arise
if the data produced by ・The original motivation
for the activity might be
individuals areconcentrated on certain lowered due to themonetary consideration.
companies.

Data Labor

Source：Prepared referring to Imanol Arrieta-Ibarra, Leonard Goff, Diego
Jiménez-Hernández, Jaron Lanier and E. Glen Weyl.(2018) ”Should We Treat Dataas Labor? Moving Beyond ‘Free‘,” American Economic Association Papers &Proceedings, Vol.108, pp.38-42. and other referential information, and discussionin Special-Interest Group
Source: Prepared through publisheddocuments

Table 5: Issues on the approach of “paying remuneration for the users who produced data”
Data as Capital
Data as Labor
Paying remuneration for
the users who produced
data

Positive

Reasons for paying
remuneration/not for
paying remuneration

・Free data sets andmachine learning algorism
・With the advance of AI,
are freely available
data will be more valued,
・Data can produce value
so the incentive to provide
only when it’s analyzed, so
data will be needed.
data analysts should be・Existence of enterprises
paid.
that take most of the value
・Giving monetaryproduced through data as
consideration might lower
private profits
the original motivation for
activities.

・”Data as labor” can be an
important opportunity tocomplement the income of
citizens who suffer fromthe expansion of economic
disparity, and bring aboutawareness of contribution
to society.

Process for paying
remuneration

Market mechanism(Micropayment/information
trust function (Informationbank))
・Regulation (competition
policy/tax system/social
security, etc.)

・(Limitations) Build an
appropriate technology
system to estimate thecontribution andretroactively track thevalue of data individual
users produced.

Expected issues

・Possibility that uneven・Market mechanism:
distribution of wealth
Establishment of businessbetween enterprises/datamodelsanalysts and other・Regulation: Objective
individuals (further decline
correctness of introductionof labor distribution rates)(quantitative index)
might arise

Negative－

Positive・(Limitations) Estimationof contribution andfeasibility of technology
・Possibility that estimationof contribution might bringabout surveillance society
and further disparity/
discrimination

Chapter 3 Data Economy Policy in the AI era
The current status and issues toward marketing and visualization of data transaction
The current status of data transaction in Japan While initiatives of marketing/visualization of data transaction are proceeding, the current status of data transaction in Japanis as follows.
① Data transaction with other companies is inactive.
② Organizational data have different statuses of providing/receiving data, depending on the type of data
③ The use of data on individuals is asymmetric between the sides of providers and receivers④ Individuals are concerned about their data being collected and used.
⑤ Enterprises tend to be afraid of being criticized for their data transaction by society.
⑥ Individuals are expecting enterprises to ensure the purpose of using data and data security.

Based on the current status of data transaction in Japan,
consider the issues to be addressed

Issues to be addressed for marketing and visualization of data transaction

 Standardization of contract formats concerning data transaction for the utilization of data
① Contractual coverage according to sales channels/price negotiation/manufacturers’ responsibility② Necessity of grasping the distribution/quality/ traceability/ freshness, etc. of data
③ Identification and fidelity guarantee of persons in charge of data labeling/annotation
 The necessity of management of data owned by enterprises by a third party
 The necessity of management of personal data by a third party (Personal Data Trust Bank’s potential)
 Finding ‘hidden needs’ in data transaction

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Chapter 4 Future Image (The Image of “Inclusive AI Economy Society")8

The image of “Inclusive AI Economy Society" In ”Inclusive AI Economy Society“, it is desirable that all the entities including individuals, small and medium-sizedenterprises, and local enterprises actively engage in social and economic activities in utilizing AI and data iningenious ways based on their intentions and judgment, contribute to the improvement of productivity, acquire
the distribution according to the contribution, fulfilment, and expansion of leisure time, and share the affluencein the entire society.
 In the analysis of the perspective of the utilization of AI, the utilization scenes of AI in individuals, enterprises andgovernment are classified into 9 scenes (Use in urban areas, Use in rural areas, Use at home, Use in research anddevelopment, Use in manufacturing/construction, Use in distribution and sales, Common use (people, goods,
and money, National government/local government use and How to work and spend leisure time.
Table 6: Perspective of the Utilization Scenes of AI

End-users

Personaluse

Outdoors
Indoors

Corporate use

Use in urban areas

Use in rural areas

Use at home
Use in research Use in manufacturing/
construction
and development

Use in distribution
and sales

How to workand spendleisure time

Common use (people, goods, and money)
Government use

National government/local government use

Analysis as a world view of where
AI has been fully utilized.

Chapter 4 Future Image (The Image of “Inclusive AI Economy Society")

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Examples of the Utilization Scenes of AI
 We looked at the utilization scenes of AI in individuals, enterprises, administration and how to spend leisure time.
Table 7: AI Utilization Scene (Example): Individuals/Enterprises (Workstyle/How to spend leisure time)Chapter 5 Recommendation

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The ideal policy toward the promotion of the utilization of AI/data
The ideal directivity
 Various restrictions brought about by COVID-19 have changed the workstyle and lifestyle of individuals and theway enterprises should be, so there is a possibility that the “new normal” might be produced.
 The transition to the economy society based on the utilization of AI is more likely to be accelerated in the “post
COVID-19 era”. Actually, the “social intelligence” that humans should display in the ”Inclusive AI Economy
Society“ has been already required.
 In this situation, measures to support enterprises, individuals and administration are required.
① In enterprises, supporting activities to encourage SMEs and local enterprises to launch this initiative for theutilization of AI/data is needed.
② In individuals, promoting public-relations campaign to rightly understand new technologies such as AI isrequired.
③ In administration, it is necessary to work on further preparation for the systems so that enterprises andindividuals can utilize data trustworthily, and it is important to accelerate the initiatives of the digital
government that can increase opportunities for private citizens to utilize public data.
Take specific measures based upon the ideal directivity
Specific measures● The support for SMEs and local enterprises to promote the utilization of AI/data● Enlightenment activities by the government and private sectors for right understanding of new technologies such as AI● Initiatives for sharing the image of ”Inclusive AI Economy Society"● Review of the system that promotes that utilization of AI/data.
● Promotion of the utilization of AI/data keeping the “post-COVID-19” society in mind.

Chapter 5 Recommendation

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The ideal way of data economy policy required for the promotion of data-driven economy in the AI era
The Ideal Way

It is expected that data-driven economy will grow going forward, but it’s hard to say market mechanism in data transaction is fully working, so it isdesirable to reach efficient resource allocation based on market mechanism as soon as possible.
The mechanism where data produce added value is complicated, and the stakeholders in the mechanism are roughly divided into four; resource
data producers, data analysts, data owners and data transaction mediators.
Resource data producers (Netservice users)

Data analysts (Datascientists)

Data owners(enterprises/platforms etc.)

Data transaction mediators(Personal Data Trust Bank,
etc.)

Reasons for paying/not paying
remuneration

Approval of the contribution
by resource data producers

Contribution to analysis

Revenue from data

Commission fee from datatransaction

Payment methods ofremuneration (process)Free services, information
provision、points, cash, etc.

Evaluation for analysis results

Business revenue

Business revenue

Standard of remuneration

Intention of data owners/datatransaction mediators

Labor market

Advertising market, etc.

Cross trading From the perspective of revitalization of the transaction of data on individuals, it is important to raise individuals’ awarenessof managing the data independently, complying with the rule for dealing with personal information. However, it should be
noted that various costs are needed for the compliance of the said rules.
 The economic characteristics of data should be considered to conduct appropriate data economy policy.
Take Specific measures based upon the ideal directivity

Specific measures● Building data infrastructure● Realization of data cooperation● Promotion of corporate data management by a third party/initiatives of Information Bank
● Review the legal system to promote the utilization of dataChapter 5 Recommendation
The Ideal way of data economy policy required for the promotion of data-driven economy in the AI era
Issues in the estimation methods of the value of data To realize data transaction markets, observation and analysis of the reality surrounding the data are important.
The empirical analysis for estimating the value of data in Chapter 3 is the clue to that.
 Data are added to capital equipment that expands the capability of workers, and are positioned as a providerof new capabilities. Therefore, it is necessary to position data as a factor of production (stock) as the beginningof a theoretical study, and organize them theoretically.
 The issues in the estimation methods of the value of data are organized and the directivity for further brushup is shown in the following.
① Building capital stock based on the economic characteristics of data.
 As the economic characteristics of data, temporal changes in the value of data and expansion of value due to data
accumulation are cited, so how capital stock in the target year should be built needs to be considered through the statusof owning and utilizing data, taking these characteristics into account.
② Further analysis of the relationship of data and the value created by data
 Regarding the relationship between quantity or quality of data and the value of data, analysis by industry type andproduction process is needed. Conducting a multifaceted analysis with more survey data is required, but to do so, building
the system where information is continuously collected in a large scale needs to be considered.
③ Social enlightenment toward data economy
 To further polish the estimation methods of the value of data, the improvement of social recognition of the value of data
and the expansion of initiatives of analysis are needed. To realize that, preparing and publishing data that can be utilized
for analysis is important. One of the methods to do that is publishing data on official statistics or financial statements.

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