AI Algorithm Transparency Toolkit
A Proposal for a Governance System to Enable Society to Accept and Benefit from AI-based
Innovations
YUKI Tonfi*†, OKAMOTO Masayuki‡, MURAYAMA Hiraku, YAJIMA Kirihito§,
NISHIGAKI Yuta** ††Summary
To optimize the benefits of AI innovation, it is crucial to establish and manage a flexible technical,
organizational, and social framework that allows stakeholders to acknowledge AI-related risks andadapt their interests accordingly—an agile governance system for AI. This paper addresses thecomplex and contentious issues surrounding AI governance, focusing on the organization of
overarching and systematic approaches for the transparency of AI algorithms, and proposes apractical toolkit for implementation.
In the absence of a systematic organization, regulations and norms are formulated for various
problems arising from innovation, leading to a continuous addition of rules without a clear blueprint.
This approach lacks predictability and reduces "transparency" to a mere formality, deviating from its
original purpose. To ensure that businesses and government agencies can effectively apply theseregulations, it is imperative to pursue a systematic approach founded on a unified concept,
generality, clarity, and flexibility, allowing for discretion in decision-making.
This toolkit comprises a comprehensive set of disclosure items and case studies, taking into
consideration discussions among international regulators, a wide array of risk events generated byvarious AI algorithms, and prior research on AI algorithms. It also embraces the perspective that thelevel of transparency achieved by businesses and government agencies can positively impact not
only their societal credibility but also other metrics, such as satisfaction for the product that the AI
algorithms are incorporated.
Designed to be highly user-friendly for both businesses and government agencies engaging in selfand co-regulation, the toolkit presents disclosure items in a list format, allowing for discretion andflexibility when considering AI algorithm providers, users, and risks. Additionally, it includes
practical use cases to further enhance its applicability.
Table of Contents
1 Why transparency of AI algorithms is required
2 Toolkit Overview
3 Toolkit Description
4 Perspectives for using the toolkit
5 Use Cases
6 Contrast this toolkit with the regulatory situation in each country
7 Conclusion
8 Acknowledgment**

SmartNews, Inc. / ZeLo, a Foreign Law Joint Enterprise(Main email address) tonghwi.soh@gmail.com‡
Toyota Motor Corporation§
Aflac Life Insurance Japan Ltd. / Aflac Digital Services Company Ltd.
**
Graduate Schools for Law and Politics, Faculty of Law, The University of Tokyo††
Program assistant
†

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1

Why Transparency of AI Algorithms is Required
1.1 Introduction
This paper proposes a solution from the perspective of transparency of Artificial
Intelligence (AI) algorithms in order for society to control the risks and adverse effectsof AI that are becoming apparent and to maximize the benefits of AI in this age when AI
has naturally penetrated society as a result of the third AI boom. Innovation often
disrupts existing systems, structures, value systems, business models, etc., in some way,
and there is no end to the misfortune caused by innovation being stifled as a result ofsociety's excessive fear of the risks involved. A report published by the Ministry of
Economy, Trade and Industry (METI)１ in 2021 describes the need for agile governanceof innovation as "the design and operation of technological, organizational, and social
systems by stakeholders with the aim of maximizing positive impacts while managing
risks arising in society at a level acceptable to stakeholders.” In order to balance AIbased innovation and the risks it entails, risks need to be appropriately distributed among
stakeholders at an acceptable level. As an example of such governance, the perspectiveof "transparency" is required. Transparency will help maximize the impact of innovationby controlling AI risks to an acceptable level and building trust throughout society
through appropriate communication with society and users.
However, although the topic of transparency of AI algorithms has already been
discussed internationally, there are a number of different proposals. While ensuringtransparency is important, as we will see in this paper, if the degree of transparency iswrong, the act of disclosure itself may even reduce users' trust and satisfaction. In
addition, regulations must not be added without systematic discussion each time various
problems caused by innovation are addressed. Otherwise, the means of transparency tobuild governance and trust and protect the right to self-determination will become an end
in itself, and as a result, responding to low-strength, low-quality disclosure items thathave been repeatedly added without blueprints will become the supreme objective. As
self-regulation and co-regulation are becoming more common as a way of regulation thatcan withstand the speed of innovation, a method of achieving transparency that is easy
for operators to understand and realistic for them to take voluntary action must be
proposed.
This paper addresses these issues by attempting to systematically organize thediscussions to date and proposing a toolkit version 1.0 that can withstand practical use.
Through this toolkit, we propose a systematic and convenient collection of disclosuresand case studies based on various risk events posed by AI and prior research on AI,
while also addressing the latest regulatory discussions in various countries. This paper
stands as a guide or handbook for utilizing the toolkit.
Throughout this paper, AI refers to a generic term for software and systems that "havethe ability to change their own output and programs through the process of utilization bylearning data, information, and knowledge, etc."２ Although this toolkit mainly focuseson AI algorithms using machine learning and deep learning, the discussion points are
generally the same for other AI algorithms such as rule-based and knowledge-basedalgorithms. In addition, the term “AI algorithm” in this paper refers to the process bywhich AI solves a problem, i.e., "a finite number of procedures by which AI solves a

Ministry of Economy, Trade and Industry, "Government Innovation Ver. 2: Toward the Design and
Implementation of Agile Governance," p. 95
https://www.meti.go.jp/press/2021/07/20210730005/20210730005-1.pdf (2023) (viewed January 28,
2023)
２
Ministry of Internal Affairs and Communications, AI Network Society Promotion Council, "AI
Utilization Guidelines - Practical Reference for AI Utilization," p. 4
(https://www.soumu.go.jp/main_content/000637097.pdf (viewed January 28, 2023)).
１

1

1.2

1.3

mathematical problem (a problem whose solution is fixed and computable)."３
Risks and adverse effects of AI-based innovation
To identify the issues to be addressed in this paper, we would like to look at someexamples of the risks or harms of AI innovations.
It has been noted that one social media A, has developed algorithms that maximize
user engagement metrics, which may be detrimental to the mental health of its users,
especially the younger generation. For another social media B, it is noted that the rankingof posts in feeds may be artificially manipulated, in some cases with some political
agenda, in ways that are beyond the control of users and society.
Human resource service C, which provides job matching services, has been found to
give preferential treatment to job seekers of a particular race or gender. Image
recognition service D encountered a problem in which a black person was judged to be agorilla.
In the restaurant rating information service E, a change in the rating algorithm could
cause significant harm to restaurants. The same is true for the impact of changes in thealgorithm of search engine F on media businesses and viewers.
Image-generating AI service G can produce countless similar works against the will ofa particular artist, and another video-generating AI service H has successfully interferedwith elections by producing fake videos of politicians.
Transparency: most frequently mentioned in AI principles and guidelines
Discussions have already begun on how human society should tackle the abovementioned issues. According to a report by the Ministry of Internal Affairs andCommunications４, "transparency and accountability" is listed in 35 (about 90%) of the
40 AI-related development and utilization guidelines published by governments andresearchers around the world. While some countries and organizations did not mention"privacy" and "fairness," which are other important issues, "transparency andaccountability" is the only item mentioned in all the guidelines by all countries andorganizations.

"Algorithm." Merriam-Webster.com Dictionary, Merriam-Webster, https://www.merriamwebster.com/dictionary/ algorithm. Accessed 13 Jan. 2023.
４
Ministry of Internal Affairs and Communications, AI Network Society Promotion Council,"Report 2022 - Further Promotion of 'Safe, Secure and Reliable Social Implementation of AI'" (July25, 2022), p. 78 https://www.soumu.go.jp/main_content/000826564.pdf (2023, Jan. 28, viewed on
January 28).
３

2

Table 1 AI Network Society Promotion Council, Ministry of Internal Affairs and Communications,"(2) Verification based on comparison with principles, guidelines, etc. overseas" (cited from "Report2022: Further Promotion of 'Safe, Secure, and Reliable Social Implementation of AI'", p. 78)

1.4

Beyond these guidelines, the EU has already initiated regulations to achieve some AI
transparency with the General Data Protection Regulation (GDPR)５ and is working on
further regulations such as the AI Act６, the Digital Services Act (DSA), and the DigitalMarket Act (DMA)７. Japan has also introduced co-regulation in terms of transparency in
the Act on Improving Transparency and Fairness of Digital Platforms (DPF
Transparency Act). Already, transparency of AI algorithms is not an ideal that should be
realized someday but a demand from society that should be addressed in reality.
Possibility of resolving issues through transparency
Why is transparency in AI algorithms being called for so loudly?
In order to create a society in which humans are in harmony with AI, humans need tounderstand AI. Innovation, not limited to AI, comes with risks, and those risks must be
evaluated and controlled so that innovation can permeate society. For humans to coexist
with the risks of AI, it is essential to foster trust in AI. Human society must be able tounderstand that AI is safe and secure if handled properly. For example, food products we
eat are labeled with information such as nutritional content, ingredients, country oforigin, and expiration date. This transparency is necessary to ensure the safety andreliability of the food products we purchase. More theoretically speaking, it is a measure
to protect the right to self-determination. The examples of risks and adverse effects of
AI-based innovation described at the beginning of this paper can find solutions if
businesses consciously communicate with society about the risks posed by AI and how
to control them. A discourse exists that AI is inherently value-neutral and moretrustworthy than humans, but it misunderstands the nature of the problem. It has been

European Commission "EU data protection rules" https://commission.europa.eu/law/lawtopic/data-protection_en ( (viewed January 28, 2023).
６
European Commission "A European approach to artificial intelligence" https://digitalstrategy.ec.europa.eu/en/policies/ european-approach-artificial-intelligence" (viewed
January 28, 2023).
７
European Commission "The Digital Services Act package" per DSA, DMA respectively
https://digital-strategy.ec.europa.eu/en/ policies/digital-services-act-package (viewed January 28,2023).
５3

pointed out that it is humans who develop AI, and as a result, introduce "systemic
biases"８ and "reinforce historical discrimination."９ The issue is not whether AI is moretrustworthy than humans but rather the transparency of AI as a basis for individuals andsociety to judge whether AI developed by humans can be trusted.
However, transparency of AI algorithms does not prevent businesses from offering
benefits to individual users and society. Rather, businesses that develop and provide AI
services can expect to benefit from them too. Businesses can identify the factors thatdifferentiate them from their competitors by ensuring the transparency of AI algorithms.
Continuing to use the food analogy, this is similar to using organic and pesticide-freevegetables and direct delivery from the place of production as differentiating factors.
Efforts to achieve transparency can also be linked to risk management for businesses.
The toolkit proposed in this paper can serve as a checklist to examine whatorganizational or technical measures to take after systematically organizing the risks
posed by AI. By determining how businesses can control the assessed risks andcommunicating with society, it will be possible to work with society to resolve any risks
that become apparent. As a result, for businesses that develop and provide AI, it isequivalent to practicing the risk management process from an overarching perspective.
Businesses sometimes express the concern that disclosing their AI algorithms maylead to the risk of being hacked by users or of trade secrets becoming known tocompetitors. However, the transparency of AI algorithms proposed in this paper does notmean disclosing the code in detail. Such disclosure would not explain transparency to themajority of users, as only a limited number of researchers and developers would be able
to understand what it means. This paper proposes, based on international discussions, amore abstract and logical explanation of features and their weighting. What is required is
not the disclosure of a secret sauce recipe, but rather the disclosure of the ingredients,
cooking method, and seasonings used for the dish.
2

Toolkit Overview
2.1 Purpose and role of the toolkit
As we saw in the previous section, although the need for transparency of AI
algorithms has been advocated in various places, concrete measures have been left tovoluntary efforts or preceded by specific regulations in individual countries, and practicehas begun to progress without an overall systematic organization and understanding. The
toolkit proposed in this paper (the "Toolkit") aims to serve as a catalog of responses with
contents that can withstand practical application, based on a systematic organization of
the transparency of AI algorithms, reflecting the latest discussions. In turn, the issues thateach country's regulations and self-regulations by operators seek to resolve can beunderstood from a bird's-eye view by referring to this Toolkit.
The reason for proposing a toolkit rather than principles and guidelines fortransparency is that the content and degree of transparency required will vary dependingon the entities, purposes, methods, and situations in which AI is used. The aim is not a
grand ideal, but a catalog that can be used by each stakeholder depending on the context.
Therefore, this toolkit is a collection of options to ensure transparency and should not betaken as a strict disclosure item. Let us explain how to use the toolkit from theperspective of building trust and communication between the business that develops andprovides AI and the users and society.

Kashin, K., King, G., & Soneji, S. (2015). Systematic Bias and Nontransparency in US Social
Security Administration Forecasts. Journal of Economic Perspectives, 2(29), 239-258 .
(https://www.aeaweb.org/articles?id=10.1257/jep.29.2.239 (viewed January 28, 2023))
９
Kroll, J. A. (2015). Accountable algorithms.(Doctoral Dissertation) Princeton University(https://dataspace.princeton.edu/handle/88435/dsp014b29b837r (viewed January 2023) (viewed 28
January 2023))
８

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2.2

2.3

Scope of the toolkit
As mentioned in Section 1.1, this paper refers to AI in terms of software and systems
using machine learning models and deep learning models. It does not restrict the
purposes or objectives for which AI is used, so its contents can be widely used for all
software and systems using AI.
However, there are some issues with AIs that cannot be solved with transparency. In
particular, manufactured goods or products equipped with AI that involve risk to life orbody, or services for which users have few alternatives (e.g., automated driving, digitalplatforms, etc.) must pursue the safety of users in the first place, and transparent
communication about the existence of risk alone will not lead to practicalimplementation.１０ In this sense, this toolkit is structured on the premise that AI and risks
go hand-in-hand.
Structure of the toolkit
As shown in Figure 1, algorithms of machine learning models are different fromtraditional programming, in which humans develop an algorithm by specifying
commands and conditions, in that the model itself is generated by a machine based on
data.１１ Since an understanding of this point is essential for the use of this toolkit, we
would like to review the differences between the two specifically using Figure 1.

Even if the "risk of this self-driving car getting into an accident" were disclosed, the only thing
the user can do is not get in that car.
１１
We would like to clarify the definitions of "algorithm" and "model" in machine learning, just to
be clear. A machine learning algorithm is a procedure that learns a processing method from input
data or returns a processing result in response to input data, depending on the purpose, such asclassification or prediction. Depending on the purpose, algorithms such as logistic regression,decision trees, and neural networks are used. A machine learning model is a program that outputs
some kind of identification or judgment result for new input data, which is the result obtained fromprocessing training data by a machine learning algorithm. Even if the same algorithm is used, theoutput model will be different if the data to be learned is different. The terms used in this text arealso based on this definition.
１０

5

Figure 1: Comparison of traditional programming and machine learning algorithms (adapted fromZEISS１２)

In traditional software development, such as rule-based programming, the output (theresult of recognition and inference; "Result" in Figure 1) is obtained as the result ofprocessing data by a program created by an expert on a computer ("Handcrafted model"
in Figure 1).
In the case of training phase of machine learning algorithms, on the other hand, amodel showing the input-output relationship (“Model” in Figure 1) is obtained from asample of input data and expected output (correct answer) for that input (training data;"Sample Data" in Figure 1). A machine learning algorithm is used for obtaining themodel from the data. This model is then used to obtain recognition or inference results("Result" in Figure 1) for new input data. As a simple example, the curvef(x)=w0+w1x+w2x2 ... + wMxM (w0, ..., wM are coefficients, xn is nth power of x), themethod of estimating coefficients from a given combination of (x,f(x)) (e.g., leastsquares method) is the trained algorithm, and the function to which specific coefficientsare fitted is the model of the learning result.
Furthermore, human involvement continues to be significant in that the entity
developing the model checks the validity and quality of the output (the results extracted
by the model), develops additional models, and repeats this process. In Seaver's words,"an algorithm is not one small independent box, but a large network of hundreds ofpeople who tune the system, replace parts, and experiment with new arrangements."１３
Therefore, simply disclosing the code of the model or explaining only about the model
does not solve any problems.
This toolkit views AI algorithms as being formed by the organic coordination of thefive elements of data, models, outputs, human involvement, and overall system design.
As shown in Appendix 1, we attempt to ensure the transparency of the entire AI based onthese perspectives. The details of each item are described in the following sections.
3

Toolkit Description
In discussing the transparency of AI algorithms, we would first like to outline thisdevelopment and delivery process based on Figure 2.

ZEISS, "The Relation between Computer Vision and Machine Learning,"
https://blogs.zeiss.com/digital/page/2/ (viewed January 28, 2023).
１３
Seaver, N. (2019). Knowing algorithms. DigitalSTS, 412-422. https://digitalsts.net/wpcontent/uploads/2019/03/26_Knowing-Algorithms.pdf (viewed January 28, 2023)
１２

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Figure 2: Overview of the AI development process (prepared by the authors)

First, training data (Training Data) is needed to develop AI models. Based on the training
data, the AI constructs a model, and then checks whether the model can withstand actual
operation using test data. Then, in actual operation, data (Operating Data) is input to the modelas actual input, and the model produces an output (Output) based on the input. All of theseprocesses involve human involvement (Human). The concept and design that crosses each ofthese elements is referred to as the overall system design (System) in this toolkit.
Appendix 1, “Transparency Disclosure Items List for AI Algorithms,” lists the disclosure
items in ascending order based on the broad life cycle of AI development, and specifies whichof the above five elements each item is related to. The following section looks specifically at
the transparency that should be realized for each of the five elements. For convenience of
explanation, we will look at data, models, outputs, overall system design, and human
involvement, in that order, but this order is basically random.
3.1

Data
3.1.1 Overview of training data
In developing an AI system, an overview of the training data used (including data
used for testing. Same below) is disclosed. For example, for a text generation AI,
the training data used may vary depending on the large-scale language model used.
For image generation AI, the characteristics of the image dataset used should be
described in a form that is easily understood by users and the general public.
It is also important to devise different disclosures for different learning methods.
For example, in the case of supervised learning, what kind of data is used astraining data and what is learned? In the case of unsupervised learning, what is themodel expected to do? In the case of reinforcement learning, it may be necessary to
clarify the incentives to be given to the model.
Even if it is not possible to disclose all variables and items that build the data set,
it may be necessary to clearly state if there are any special or sensitive items
involved.
3.1.2 Sources of training data
Provide access links to datasets that are publicly available in a form accessible tousers and researchers.
If the data is open data, this should not be too difficult, but if not, a sampleddataset that is consistent in content with the overall dataset could be provided.
3.1.3 How the training data was obtained7

Describe how the learning data was obtained.
In most cases, the personal information protection regulations of each country
already require disclosure of the acquisition method and purpose of use of personal
information, at least at the acquisition stage. This disclosure should also be requiredat the AI use stage. In addition, there may be cases where data other than personal
information (e.g., statistical data, text data) should be disclosed as well.
3.1.4 DEIB (diversity, equity, inclusion, and belonging) policy for training data
Describe the policy and views of the business regarding diversity, equity,
comprehensiveness, representativeness (is the AI appropriately representative of the
population for which it is intended to be used), and inclusiveness that the training
data takes into account in developing the model for the AI.
When an AI has bias, one of the factors is that the training data itself is biased.
Therefore, it is important to explain how bias is eliminated so that AI can withstandactual operation, or if bias remains, to explain the reason for it. This will not only
contribute to the convenience of users, but also enhance the awareness of service
providers during development.
For example, when providing AI for the Japanese market, a dataset that is clearlybiased for the U.S. society may be acceptable from the perspective of diversity andinclusiveness in Japanese society. In such a case, it is important for the provider toclarify the results of its consideration of sufficiency from the DEIB perspective inthe relevant market, rather than pursuing the use of a diverse data set that would be
acceptable to any society.
In addition, explanations regarding diversity, comprehensiveness, andrepresentativeness may be possible by explaining data that were intentionally
excluded from the training data.
3.1.5 Operational data
Describe an overview of the data that will be used when AI is actually utilized.
If all of the items related to training data described above can be applied tooperational data as well, they can be organized into one large item called "data". On
the other hand, if, unlike training data, data directly input by the user, for example,
is used as operational data, a separate description of the data actually used may berequired from training data.
3.2 Model
3.2.1 Phases of use
Explain in which phases the AI models is used in the development of products
and services. For example, by clearly indicating the phases such as ideation phase,research phase, beta phase, or production phase, users will be able to adjust their
expectations and usage of the AI.
3.2.2 Learning method
Describe the type of learning method used to develop the model. The description
and granularity of these explanations – on topics such as machine learning, deeplearning, expert systems, rule-based, etc. - should be customized on a case-by-casebasis, adjusting to the understanding level of the users and researchers.
3.2.3 Explainability
AI creators should describe their understanding of the model to the extent thatthey can.
For example, due to the black-box nature of deep learning models, there could becases where verbalizing abstract parameters and features is not entirely possible.
However, considering that the purpose of transparency is to build trust in AI
products and to protect their rights, including their right to self-determination, there
are many issues that can be resolved by ensuring transparency by providing somelevel of explanation at a granularity level that is understandable by humans.”
Explainable AI (XAI), which has been introduced in recent years, is a notable
example of such efforts. It is safe to assume that such resources can help offload
8

excessive accountability from operators and offer alternative ways to explain AI
models on the behalf of people.
3.2.4 Parameters/Features and their purpose/reason
Describe the parameters/features that make up the corresponding model. It ispreferable to explain the operator's understanding of their purpose and reason forusing a certain model. A parameter or feature is a quantitative expression of thecharacteristics of a data set. This paper does not seek to strictly define them, butrather to define them in terms of the quantifiable characteristics and settings that AI
algorithms take into account when generating certain output results. The
quantification of which parameters are important is called a weighted parameter (tobe explained later). In AI product development, the main issues are how to design
the parameters or obtain them from data, and how to set or optimize weights. A
popular example used is an AI algorithm that is used to predict ice cream sales. An
AI model is built by learning the relationship between sales and various factors suchas weather, temperature, number of products, and location (e.g. distance from thenearest station). Each of these factors make up a parameter. The parameters of themodel will be determined by the number of products and sales data. As is often seen
in deep learning, there may be cases where effective parameters are obtained as themodel learns through the data, in addition to people manually designing individual
parameters.
However, it is not required to disclose all detailed parameters and otherinformation. Disclosure of detailed parameters may make it difficult to understandfor users and lead to unintended disclosure of a business’ “secret sauce”. Rather, it
is most practical to verbalize the important parameters considered by the model,
which are abstracted at a level that is understandable to users, together with theweightings described in the next section. Going back to the previous example, users
do not require an explanation of “the weather classified into 18 types, based on thefollowing technical definitions.” Instead, they can understand what is necessary via
a simple “today’s weather” parameter. When the AI is learning parameters using
deep learning, it is possible to verbalize and disclose only the learning
methodology. In addition, by explicitly explaining factors that do not affect themodel but could easily be misunderstood as having an impact on the model cany
help facilitate appropriate usage of AI algorithms.
Perhaps the most informative regulation in this regard as of January 28th, 2023, isthe EU Regulation on platform-to-business relations (P2B Regulation)１４. The
Regulation requires online platform operators to achieve transparency of key
algorithms and parameters in order to achieve fairness and transparency intransactions between operators, including SMEs (small and medium-sizedenterprises) on online platforms. Specifically, the Regulation requires that key
parameters which affect rankings be determined and explained. If there are many
parameters in the AI algorithm, the practice is to classify them into several
categories, identifying major categories that play a decisive role. It is also
considered best practice to describe the decision-making process itself leading up to
the determination of the major parameters. In addition, a list of commonly usedparameters is published１５, making it easy to understand what are potential"parameter" descriptions. For example, "quality of content (reciprocal links to
websites, richness, quality and number of languages supported)," "geographic
European Commission "Platform-to-business trading practices" https://digitalstrategy.ec.europa.eu/en/policies/platform- business-trading-practices" (viewed January 28, 2023).
１５
EUR-Lex "Guidelines on ranking transparency pursuant to Regulation (EU) 2019/1150 of theEuropean Parliament and of the Council "(Annex 1) https://eur-lex.europa.eu/legalcontent/EN/TXT/?uri=CELEX%3A52020XC1208%2801%29 (viewed 28 January 2023).
１４

9

proximity," "popularity of products and services," and "availability of inventory"
are listed.
Although the Digital Platform Transparency Act in Japan also requires disclosureof key parameters, the P2B Regulations are unique in that they further require an
explanation of the reasons for disclosure. Depending on the type of AI algorithm, if
the purpose, reason, and intent of the AI algorithm can be disclosed, users andsociety will be able to understand how to utilize the AI algorithm in a deeper
manner. The following are some examples of actual disclosures.
For example, Google LLC, on its official website１６, lists "user relevance,""quality of app experience," "editor rating," "advertising," and "user experience" as
key factors to explain the app rankings in Google Play. Having said that, Google
notes that “These main factors impacting ranking are weighted differently based onwhere on Google Play a user is looking, the device they are on, and their personal
preferences.” In order to promote a deeper understanding of each item, the report
goes on to explain in detail why and how such factors were chosen as "key factors"
in the study.
For merchants, Amazon Japan G.K.１７ discloses that the main parameters that
determine the product ranking in its Amazon Listing Services online mall arefactors such as the match rate of text and other information with the productinformation, price, points, stock availability, product line, and sales history". For
general users, it discloses the following: "customer behavior (e.g. frequency of
product purchases), product information (e.g. product name, price, productdescription), availability, delivery time, various fees (e.g. delivery fees), andwhether the product is of potential interest (e.g. a new product). The site is uniquein that it places different explanations for merchants and for users.
On its official website１８, Yahoo Japan Corporation mainly considers productswith a speedy reliable shipping icon in the mechanism for determining searchrankings and what is displayed in particular slots. Furthermore, as "other factors to
be considered," it states that "the relevance of search terms, the number ofpurchases, the number of customers who purchased the product, the number of units
sold, the number of product reviews, the number of store ratings, the number ofstore ratings, and the number of store customers who purchased the product are allconsidered. At the same time, the report addresses potential misunderstandings by
clearly stating that "payment of sales promotion fees, etc. from store owners does
not directly affect the ranking position.” In addition, the report１９ carefully explains
the reasons and perspectives that led to the extraction of these key parameters.
Namely, "we extracted the major factors that determine the display order of ouronline mall's 'Recommended Order' from approximately 120 factors that have asignificant degree of influence on the order. This is based on the policy that factorsthat can be used as a reference by store owners who wish to improve their display
order should be made public. We have extracted the factors that have the greatest
impact on ranking from among approximately 120 factors. We have made the items
as consistent as possible with the numbers available in the store tools (number of
Google LLC, "App Detection and Ranking," https://support.google.com/googleplay/androiddeveloper/answer/9958766?hl=ja (viewed January 28, 2023)
１７
Amazon Japan G.K., "Summary of Periodic Reports by Specific Digital Platform Providers for
Fiscal Year 2021," pp. 50, 66 https://www.meti.go.jp/press/2022/12/20221222005/20221222005c.pdf (January 28, 2023) (Viewed)
１８
Yahoo Japan Corporation, "Introduction to Transparency Initiatives," https://businessec.yahoo.co.jp/shopping/digitalplatformer/ (viewed January 28, 2023)
１９Yahoo Japan Corporation, "Summary of Periodic Reports by Specific Digital Platform Providers
for FY2021," p. 43.
１６10

customers, number of orders, number of reviews) and the scores disclosed as "store
performance" (number of orders, average store rating score, etc.), so that store
operators can easily improve their store.” Furthermore, as an explanation based on
the characteristics of the machine learning algorithm, "since the content andweighting of the factors that determine display rankings change daily throughmachine learning, it is almost impossible to accurately disclose all of them at all
times, but when we add a new major factor that has a large impact on rankings, wealways update the disclosure.
3.2.5 Weights and their purpose and rationale
This section describes the weights or coefficients for each parameter. Weights arecoefficients that quantify which parameters are important. In the previous example
of ice cream sales forecasting, if the emphasis is on temperature, the weight for theparameter representing temperature will be increased, and if the emphasis is on
location, the corresponding weight will be increased. The weights may be set by anexpert on the problem that needs to be solved, or they may be adjusted andoptimized by learning from the data. As with the parameters and features, theweights should describe the operator’s understanding of the purpose and reason for
the weights.
Parameters are not often dealt equal to each other in the model, and an
explanation may be required that takes into account their interrelationships andrelative importance, so that users can understand the differences in importance
between parameters. However, since weightings can change on a daily basis anddetailed adjustments are often kept secret within a business, a description of relative
relationships is often preferred over individual existing descriptions.
The EU P2B regulation, for example, does not require the disclosure of precise
weighting indices or coefficients between parameters. Since what is beneficial tousers is being able to assume how parameters should behave, limiting the disclosure
to relatively important parameters would be sufficient to achieve this purpose.
Therefore, instead of describing this item independently, a possible method is to
extract parameters that have a large impact based on relative comparisons amongparameters as major parameters or important elements, together with the parametersand characteristics in the previous section. Parameters are important because theyaffect the output extracted by the model (e.g. display rankings, rating results,
screening results, judgment results, etc.), the matters that are particularly importantto the operator (through the model, the operator's thoughts and philosophy on the AI
algorithm are reflected), or one particular measure would be the factor that should
be considered by the user for optimal use of the AI algorithm (through the user's
optimization behavior, the operator's ideology/philosophy to the AI algorithm isreflected).
Some of the disclosure cases already mentioned in the previous section havebasically adopted the policy of explaining key parameters, which seems to begeneralized in the future. On the other hand, for AI algorithms used by publicorganizations or AI algorithms that are considered to have high risks, not only themain parameters but also the relationships among them should be described, and insuch cases, separate and independent explanations should be provided.
3.2.6 Different treatment for different user categories, including billingThis section explains how different treatment may be given to different
categories of users, in cases where parameters and weighting may vary dependingon the billing or other actions or attributes of the user. For example, there may becases in which different treatment is applied to categories based on whether or not
the user is charged or different categories based on service provider, age, gender,
total usage period/frequency, and so on. This also includes explaining when aservice operated by a service provider (including its group companies) and otherservices are treated differently in the AI algorithm.
11

In fact, the EU P2B Regulation has a regulation that requires disclosure of how
the direct or indirect payment affects the ranking algorithm. Similarly, regulations
regarding in-house preferences are also included in the P2B Regulation and the
DMA.
For example, let us assume that a platform operator uses AI to adjust its
recommendation engine and ratings. A social media platform may disclose that itprioritizes the posts of charged users in terms of display order and that it makes it
easier for replies and shared posts to be displayed. There are also operators thathave policies that do not use certain AI algorithms for users of a certain age.
3.2.7 Proper Procedures to Make Changes
This describes the process to be followed by service providers when AI
algorithms are changed.
If changing the AI algorithm substantially affects users, e.g., affects ratings orchanges the results of recommendation engines, prior notice, purpose of the change,and expected impact of the change should be described from the viewpoint ofpredictability and protection of users. Naturally, it is possible for users to misuse
the information (e.g. to abuse the information). Since abuse by users (engine hacks)
should be prevented, prior notification may be abstracted to a certain degree.
3.3 Output
3.3.1 Performance Accuracy and Limitations
This section describes the accuracy and limitations of the AI model’s result
output.
For example, by describing how reliable the results are in terms of significanceprobability, error rate, and correct answer rate, users can adjust their expectations
and use of the AI appropriately.
It would also be useful for users to describe common errors and explain casesthat are prone to incorrect answers.
3.4 Overall system design (System)
Depending on the learning method used, AI may produce output with logic that isbeyond the comprehension of ordinary users. Whether the AI product can be trusted or
not depends on the transparency and accountability before and after using the AI, as wellas on the entity developing the AI product and its governance system. Therefore, thesection on overall system design and human involvement describes the human
involvement as a subject using AI, its design concept, and its governance system, inorder to gain understanding of and trust in the AI.
3.4.1 Purpose of Use
Describe the purpose of using AI in the service or system to be provided.
The purpose of use should include the background of development and future
vision, which will lead to a more concrete understanding of the business's intention
and philosophy of use.
3.4.2 Benefit or Impact
Describe the benefits or impacts of using the AI.
This item can be described from various perspectives, such as from the user's
perspective, from the business operator's perspective, or from the perspective ofsociety as a whole. By describing mainly the positive impacts that can be expected,
apart from the risks, it is possible to lead to a better understanding of users andsociety as a whole.
3.4.3 How the AI will be used
Explain how AI is used in the services and systems you provide.
How AI is used in the overall service often occurs in ways that are not obvious tousers at first glance. Specifically, it is desirable to explain in detail which processes
use AI, and whether the output of AI merely serves to support human decisionmaking, or whether the AI output itself making the decision.
For example, when AI is used in credit screening based on credit card payment
12history information, purchase history, etc., whether the screening result by AI itself
becomes the final screening decision or whether a human makes the screening
decision while referring to the screening result is completely different in meaning,
and unless the results are disclosed, users will not know.
3.4.4 Risk Management
Describe how to assess, minimize, or control risks that may arise from the use of
the AI.
Rather than concealing the risks generated by AI, the potential risks should beshared with users, and efforts should be made to minimize them or to control them,in order to coexist with the risks and generate innovation. Such a stance would
allow users to use the service with an awareness of the risks, and would also lead torisk management by the users themselves.
It may also be possible to differentiate the company's AI by explaining theadvantages of the company's AI in terms of risk management against the risks thatsimilar AIs generally encompass.
Risk management can be organized in terms of data, models, outputs, etc., tomake it easier to understand.
3.4.5 Education System
In order to avoid, as much as possible, any damage to users or third parties or
new problems to society caused by the use of AI, explain how the business has
established an education system for the ethical awareness and points to keep inmind required for the development and provision of AI, the content and frequencyof training, and the certification system. The content and frequency of training,certification systems, etc. will be explained. This will help visualize the quality
level of handling that the service provider is aiming for.
3.4.6 Audit System
Describe what kind of audit system is in place for data, models, outputs, etc.
Identify what kind of audit is conducted after the model is utilized and what kind of
feedback is provided in terms of audit entity (internal or external), audit target(audit of the development process or audit of the technology itself), etc.
3.4.7 Control by user
3.4.7.1 Choice of AI Algorithm Use
This section describes how users can choose whether or not to use an AI
algorithm, or which AI algorithm to use if there are multiple options.
For example, a social media platform makes a distinction between feeds
based on recommendation engines and feeds based solely on posting times,
and allows users to choose between the two. However, it is not always clear
at a glance whether the distinction is made and whether the user can make achoice or not. If we want to enable users to select AI algorithms proactively,autonomy must be properly communicated.
3.4.7.2 Feedback
Describe whether there is a method of feedback by users to AI algorithms
and data, and if so, how it is used.
For example, some aggregation platforms allow users to select items ofinterest and thereby provide feedback to the AI algorithms. In addition, someproviders allow users to correct or add to the profiling results inferred by theprovider, so that the AI algorithm can be adjusted to be more optimal.
3.4.7.3 Data Opt-Out
This section describes whether there is a way for users to request to stop
the use of their own information, their own intellectual property, etc., in the
training data used for AI models, and if so, how to apply for such a right.
Services and systems that assume the existence of a certain number ofusers who do not want their data to be used for learning data, which is often aproblem especially in generative AI, should consider implementing such an
13

opt-out right. While opt-out rights for the use of personal data are recognizedin the EU and some states in the U.S., the use of data beyond personal data
must be determined by the business.
3.5 Human Involvement
3.5.1 Persons responsible for development and management
The person(s) responsible for developing and providing the AI and the person(s)responsible for its management shall be listed. This statement clarifies where
responsibility for the AI should be attributed. This is in line with the international
trend of making the clarification of parties responsible for handling personal
information mandatory.
3.5.2 The development team’s DEIB policies
Describe the operator's policies and views on "Diversity, Inclusion, Equity, andBelonging" i.e. diversity, fairness, comprehensiveness, representativeness, andinclusiveness taken into account by the team developing the AI.
One way to minimize AI bias is to ensure the diversity of the team that develops
and delivers it, or to be consistent with the population of expected users. However,
it is rare for a development team to have the necessary and sufficient attributes in allthese aspects. Therefore, the policy of the provider could be explained in terms of
how it tries to eliminate unconscious bias, and whether a system is in place to checkfor multiple perspectives.
3.5.3 Management of contractors
When AI is developed and provided together with external contractors, themanagement methods and systems for such development and provision should be
described. For contractors, it is required to describe the measures taken to ensure
appropriate development, such as requiring each business to develop AI according
to its own guidelines.
3.5.4 Operator run monitoring and maintenanceIn particular, an active system for monitoring and maintenance by service
providers to provide quality or safety evaluation of AI produced output should be
described.
In the actual operation phase, an active monitoring system for output may berequired from the viewpoint of trust and safety in order to minimize the impact oferroneous output or illegal and/or harmful information about users. In addition,
sharing the specific operational status (e.g., number of detections) with users andthe general public, and explaining how to provide feedback about such information
to the AI will lead to further trust building.
3.5.5 Inquiry handling
Describe information on inquiry contact points and complaints against output by
users and researchers, customer experience system and its operational results (e.g.,contents and frequency of inquiries), etc.
For example, in disclosures based on Japan's DPF Transparency Act, Amazon,
Google, and others submit in the form of annual reports the types of complaints, theaverage length of time for processing complaints, and a summary of the results ofprocessing (percentage of results processed, etc.)２０ . This is a useful quality
disclosure that shows how each operator is dealing with user inquiries.
3.6 Points to be noted
Regarding points related to human/AI interaction, in particular, some business entities
that provide multiple services or products may have a unified system across the board,
not by service or product unit, but by business entity unit. In addition, there may be areas

Ministry of Economy, Trade and Industry, "Compilation of 'Assessment of Transparency and
Fairness of Specific Digital Platforms',"
https://www.meti.go.jp/press/2022/12/20221222005/20221222005.html (viewed January 28, 2023).
２０

14

in which transparency can be further improved, or disclosure may be required from theperspective of the business as a whole. However, this toolkit, as ver. 1.0, focuses on asingle service or system for the time being.
4

Perspectives for Using the Toolkit
As described above, explanations have been provided for each item in this toolkit. However,as repeatedly stated, not all entities are required to disclose all of these items in detail every
time. Important perspectives in deciding which items to disclose include the risks inherent in
the AI to be used, the entities using the AI, and the stakeholders who need the information.
4.1 Classification by risk/impact of AI use
The EU has classified AI into four risk-based categories in its AI Regulation２１ , anddoes not require uniform regulation and transparency for all AI. Naturally, thetransparency required will differ depending on the size of the impact.
Although this is only one reference, for example, the higher the level of transparency
required for Tier 1, the more transparency would be expected in the followingclassification. However, depending on the type of AI used by Tier 1, there may be issues
that cannot be resolved by transparency (e.g., when significant damage is caused once
the risk becomes apparent), so this point should be kept in mind.
risk

concreteexample4.2

Tier 1Those that endanger
the life or body of theuser or thefundamental humanrights and liberal
democratic values
shared by society.
AI to make medical
decisions, large scale
video surveillance AI,
vote destination
matching, etc.

Tier 2Any economic impact
on users

Tier 3
Other AI (thatassists users indecision making orprovides operational
support)Job matching AI for job
seekers, contract
reviewal AI, image
generation AI (forcreators), businessrecommendation engine
for restaurants andhotels, etc.

Work shift
coordination AI,
search engines, etc.

Classification by entity using AI
The transparency required will increase depending on the entity that uses the AI. In
particular, when an AI is used by a public organization in its business, it is the citizens
who are affected by its administrative services, and a high level of informationtransparency is required regarding its use. In addition, a high level of transparency is also
required for major platform operators with a dominant position in a certain market inorder to ensure the fairness of their transactions.
core

Tier 1Government and Public
Organizations,
Major Platforms

Tier 2Other

In fact, the Algorithmic Transparency Recording Standard published by the UK
The European Union "Regulatory framework proposal on artificial intelligence" https://digitalstrategy.ec.europa.eu /en/policies/regulatory-framework-ai, viewed 28 January 2023).
２１

15

government at the end of 2022 (２２ ) comprehensively organizes how the public sector,
including government, should disclose information when using algorithmic tools. Like
this paper, it does not concentrate solely on models, but also expresses the accountability
that the public sector should fulfill from various perspectives, from data and output tohuman involvement. It will be referenced in various places in the future as a standard fortransparency in light of the importance of the public sector as an entity.
4.3

The information recipient’s perspective
The items to be disclosed from the perspective of transparency of the AI algorithm
using this toolkit and the manner in which they are described must take into account thedifferent stakeholders who will review the disclosed results. This is called the reader's
perspective. This is because each stakeholder has a completely different understanding,need, and use for transparency, and the method of explanation should be individually
selected accordingly. Table 2 shows a selection of previous studies that pointed out thateach stakeholder has a different understanding of transparency regarding robotics andAI.
stakeholders
developer
user

community
Experts andregulators

deployer(deployer)

Types and Objectives of Transparency
Understand how the system works for debugging andimprovement purposes
Facilitate monitoring and testing for safety standards
Understand what the system is doing and why in order toanticipate what might happen in unforeseen circumstances, andto build confidence in the technology.
Understand why a particular prediction or decision was made,ensuring that the system functioned properly, and allow formeaningful challenges (e.g., approval of a review or a criminal
conviction).
Broadly understand the strengths and limitations of the system,
gain familiarity with it, and overcome a reasonable fear of theunknown.
Provide the ability to audit in detail the traces of predictions anddecisions, especially if something goes wrong (e.g., a collisionby an autonomous vehicle). This may require storing key data
streams and tracking each logical step, facilitating theassignment of accountability and legal responsibility.
Ensure that users are comfortable with predictions and decisions
and continue to use the system.
Induce a user to take some action or behave in some way. For
example, Amazon recommends a certain product and describes
it in such a way as to induce the user to click and purchase it.

Table 2 Differences in each stakeholder's understanding of transparency regarding robotics and AI(adapted from Weller, 2017, translated by the author from２３ )As this table reveals, each stakeholder has different objectives and different levels of
understanding, and these differ for developers, users, society in general, or experts andregulators. The selection of items in this toolkit and the manner in which explanations
GOV.UK "Algorithmic Transparency Recording Standard"
https://www.gov.uk/government/publications/algorithmic- transparency-template (viewed January
28, 2023).
２３
Weller, A. (2017). Transparency: Motivations and Challenges. arXiv:1708.01870.
https://arxiv.org/pdf/1708.01870.pdf (viewed 17 January 2023).
２２

16

4.4

are provided should be tailored accordingly.
It is also important to consider the perspective of the country/region in which thesestakeholders are located. Legal regulations, social norms, and value consciousness differ
greatly from country to country and region to region. Accordingly, there will be different
levels of transparency that should be ensured in accordance with the issues that society isaware of. This toolkit isalso intended to be used by internal audit and risk compliance departments as anotherstakeholder group. By using this toolkit as a checklist, it will help promote in-house
understanding of AI, as well as risk assessment and management of the AI in question.
Granularity and Degree of Transparency
Transparency does not mean that it is sufficient to simply disclose each item in thistoolkit. Some researchers have criticized transparency as meaningless or evenharmful２４ , and one of the strong grounds for this criticism is that there are limits tousers' understanding of the disclosure items. Explanations that are too vague or that skirt
the details do not contribute at all to gaining trust and improving understanding, which is
the goal of transparency. On the other hand, it is also difficult to agree on whether
detailed disclosure will achieve these goals, since there is a limit to users' understanding.
However, Table 3, which summarizes several previous studies on the granularity anddegree of transparency of AI algorithms, suggests a realistic possibility.
Research topics
Movie
recommendation
systems

Transparency result indicators-Acceptance of the system-PerformanceMusic recommendation
systems
Social robots-Satisfaction with the system-Reliability-Liability attribution-Attribution of credit-Acceptance of the system-Reliability-Competence

Cultural property
recommendation
systems
Action recognition
systems-Comprehension-Reliability-PerformanceMusic recommendation
systems-Mental model-User confidence

Facebook's news weed
algorithm-Degree of initial surprise, anger,
or frustration-Satisfaction with the level ofgradations.

Findings
Mixed results: noeffect on
performance, but
positive effect on
acceptance
Positive effects on
satisfaction and trust
Weak effectWeak effect: noeffect on trust andcompetence, partial
effect on acceptance
Explaining why or
why not improves
understanding,confidence, andperformance
A sound andcomplete explanation
is best forunderstanding andtrust
Knowledge of theexistence of thealgorithm produces
positive and negative
effects.

Ananny, M., & Crawford, K. (2018). Seeing without knowing: limits of the transparency ideal
and its application to algorithmic accountability New Media & Society, 20(3), 973 -989.
https://doi.org/10.1177/1461444816676645 (viewed 17 January 2023)
２４17

Peer evaluation in
MOOCs-Credibility

Environmentallyfriendly mobile apps

-Reliability-Recognized controls

Online advertisement-Reliability-Spookiness-Satisfaction

Facebook's News Feedalgorithm-Recognition-Correctness-Interpretability-Accountability

The effectiveness oftransparency depends
on users' expectations
and whether they aredisobeyed. If theresults do not meetthe users'
expectations, thelevel of trust will be
low, even if
transparency is high.
Positive effect on
confidence but noeffect on perceived
control
Explanations that are
too specific andgeneral lead tocreepiness.
Intermediate
explanations increase
trust and satisfaction.
Algorithmictransparency leads todisillusionment.
Highest effect onawareness, followed
by effect on
accountability

Table 3 Prior studies on the impact on individual users on the transparency of various AIs (adaptedfrom Felzmann et al.,2019, translated by the author from２５ )What the previous studies show is that, depending on the type and role of the AI, thegranularity and degree of transparency must be devised, which in some cases may rather
reduce users' trust, satisfaction, and performance. In particular, it is important to note that
if we provide too much information in an attempt to explain in detail, and as a result, theusers do not understand the information, it will rather promote a sense of weirdness
toward AI, or that seeming transparent, it may lower the level of trust, depending on the
behavior of AI. On the other hand, simply putting overly general explanations as an alibi
will not produce any positive effects, including PR effects. From the perspective of users
and other stakeholders, the degree of disclosure necessary to maximize effects should beconsidered depending on the type of AI and its role.
5

Use Case
5.1 Purpose of the Use Cases
We would like to note two brief use cases to illustrate how this toolkit can be used
specifically. These use cases by no means represent ideal disclosure cases, andintentionally include controversial items. We hope that the use cases will help you
consider the selection of what to disclose and how to describe it. Please note that all use

Felzmann, H., Villaronga, E. F., Lutz, C., & Tamò-Larrieux, A. (2019). Transparency you cantrust: transparency requirements for artificial intelligence between legal norms and contextual
concerns. Big Data & Society, 6(1). https://doi.org/10.1177/2053951719860542 (viewed January 17,2023).
２５

18

5.2

5.3

cases are fictitious cases, and there is no particular system or service in mind.
When AI is used by a government agency to review applications for grant programs
In this use case, the following points are factors to be considered in achievingtransparency of AI algorithms, and the items, method, and degree of disclosure should be
addressed in light of them.
(1) Entity.
XX Agency (government agency), and transparency is strongly required.
(2) Risks/impacts of using AI:
Economic impact on the user (grant application or not).
(3) Recipients of information:
The public at large, with wide variations in literacy.
In such a case, a wide range of information is required to be disclosed, and it isnecessary to devise a method and degree of disclosing information that is understandable
to the public at large, while requiring strong transparency.
An example of disclosure for this use case is provided in Appendix 2. This is by nomeans an ideal case, and for the sake of convenience of explanation, parts of thedisclosure that may require special attention are highlighted. The following is a brief
discussion of some of the items that require special attention:“Benefit/Impact" should be described as concretely as possible from the perspective ofeach stakeholder in order to gain the understanding of users. Especially in the case of use
by government agencies, cost-effectiveness of using taxpayer funds should also be
included.
In the " AI integration " section, it should be emphasized that AI does not makedecisions, but merely supports humans. It is important that it is explicitly stated thathumans are ultimately responsible for review.
The more sophisticated the "DEIB policy of development team" and "DEIB policy of
training data" are for services that are intended for use by the public at large, the moresophisticated the policy should be. In particular, consideration should be given todescribing in more detail than is currently described about measures to prevent
discrimination (different treatment that cannot be reasonably explained) based on a given
attribute.
In the " Parameters/features," five major parameters are listed. Then, a brief
description is placed for each item. For the parameter "Age and gender," which isparticularly prone to misinterpretation as irrational discrimination by the administration,
there should be a description that directs users to a separate page for more details. In
cases where differences in literacy among users are expected, such hierarchicaldescriptions are preferred.
As for "Performance accuracy and limitations" and "Monitoring and maintenance by
operators," as long as government agencies use the service, they are required to explainin advance the accuracy of AI algorithms and how to respond to possible erroneous
judgments, so that users will not have doubts about the results of the assessment.
For AI algorithms that are used by government agencies and whose results haveeconomic impact, it is conceivable that basically all matters should be disclosed in aneasy-to-understand manner, except for those that are not allowed to be disclosed due totheir nature (e.g., training data including personal data). As mentioned above, the U.K.
has taken the lead in initiating transparency initiatives by government agencies, and wewill closely follow the practical measures taken in the future.
The Use of AI Recommendation Functions for Social Media Feeds
Next, we examine the transparency of AI algorithms used for social media feeds byprivate operators. The disclosure examples in Appendix 3 show that, unlike the use casesin the previous section, there are some items that are not disclosed, while many others
are left to the user to control from the viewpoint of usability.
The "source of training data" specifies the repository where sample data is stored.
Even if it is not possible to disclose all of the training data that gives operators a
19

6

7

competitive advantage, the release of a certain amount of sample data extracted throughrandom sampling, etc., should be considered as a realistic option.
The "Parameters and features" are described in some abstracted categories for themain parameters, and the relative importance of the parameters is listed in the "Weight”.
It is also characteristic that the report specifies "as of X, 2023." Another feature is thedesign that allows users to select an individual post and learn the reason it was displayed,along with its parameters. This type of implementation is gradually starting to appear in
major social media, and it is expected to be actively used not only in social media, butalso in rating services and matching services.
Various descriptions of "control by users" are included. Ideally, the UI/UX should bedesigned so that users can notice and control feeds while using them without having tocheck such items.
Regulatory Status in Each Country vs. this Toolkit
The EU has taken the lead in discussing and introducing regulations on AI algorithms,
especially in GDPR, P2B Regulation, AI Regulation, DSA, DMA, etc. It can be seen how
important it is for the EU to consider human rights and democracy as values for the coexistenceof AI and human society. GDPR is unique in that it raises data rights to the discussion of humanrights, and AI Regulation adopts a risk-based approach, in which different levels of regulations
are applied according to the type of risks involved with AI. This paper takes the same positionin classifying the level of transparency according to the entity that develops and provides AI,
who uses the AI, how it is used, and the risks it poses. P2B Regulation, DSA, and DMA, in
particular, focus on the significant role that platform operators and online service providers playin society, and apply different levels of transparency according to size P2B Regulations. DSA
and DMA, in particular, include regulations that require the implementation of appropriate
communication with society from the perspective of transparency, depending on the size andtype of business.
Meanwhile, in Japan, the Act on Improving Transparency and Fairness of Digital
Platforms(TFDPA) has also introduced a joint regulation requiring transparency of AI
algorithms for certain designated major platform operators. As already mentioned in this paper,
voluntary disclosure by platform operators has been realized, and platform users’ understandingis also increasing.
Note that the U.S. still does not have specific regulations in place regarding AI algorithm
transparency, but discussions have already begun in Congress. The most direct example is the
Algorithmic Accountability Act (２６ ), which also calls for a high level of transparency andaccountability for high-risk cases such as AI responsible for important decision-making.
However, this bill has not yet been passed, and the U.S. Congress will continue to debate thispoint of view for the time being.
While we will not go into the details of each regulation in this report, we have prepared atable in Appendix 4 that compares the regulatory status in each country with this toolkit. Of
course, each law and regulation has a different regulatory target and different regulatory
content, so the comparison table is not intended to explain the interpretation of individual laws
and bills, but only to show, in broad terms, what items are of interest in each law or bill. We
hope you can see from the comparison table that this toolkit is calling for a bird's eye view andsystematic discussion on the topic of transparency.
Conclusion
In order to maximize the positive impact of AI innovation, it is essential to design andoperate technical, organizational, and social systems that enable stakeholders to recognize therisks of AI and to adjust their interests appropriately and flexibly. In other words, an agile
governance framework for AI. This paper challenges the overarching and systematic

CONGRESS.GOV "H.R. 6580 - Algorithmic Accountability Act of 2022"
https://www.congress.gov/bill/117th-congress/ house-bill/6580 (viewed January 28, 2023).
２６20

organization of the most controversial governance approach, transparency of AI algorithms, andproposes a practically applicable toolkit. We should avoid hampering innovative AI in social
implementation with misunderstandings caused by lack of communication, and the role thattransparency can play in this regard is not small. However, if regulations and norms are formed
each time a problem arises in the absence of a systematic organization, regulations will be
repeatedly added without a blueprint, which lacks predictability and "transparency" will
become a self-objective only for formality apart from the original purpose. As a result,innovation will be stifled. This is why such regulations must be systematic based on a unified
concept, while pursuing generality, clarity, and flexibility with room for discretion so thatbusiness entities and government agencies can actually apply them.
In this paper, we have constructed a toolkit as a collection of systematic disclosures andexamples, taking into account not only regulations proposed by national authorities but also awide range of new risk events arising from various AIs, as well as prior research on AI
algorithms. We also incorporated the viewpoint that the transparency degree that a business
entity or government agency can devise can have a positive impact not only on its social
credibility, but also on different indicators such as satisfaction. In addition, the toolkit was
intended to be highly convenient for business entities and government agencies responding to
self- and co-regulations by putting the disclosures in a list format and making it discretionary
and selective in terms of AI algorithm providers, users, risks, and other factors. We would be
more than happy if business entities and government agencies could use this toolkit forcommunication with users, society, authorities, and experts, or for internal risk management.
We believe that this is one of the best agile governance practices to maximize the impact of AI
innovation.
This toolkit is version 1.0 and we will continue to update it based on the points raised infuture discussions. We would be grateful for guidance from readers in pointing out excesses
and deficiencies.
8

Acknowledgments
Finally, we would like to express our sincere gratitude to Dr. Naoko Munakata, Dr. Hiroki
Habuka, the University of Tokyo School of Public Policy, and the students of the Innovation
Governance Expert Program for providing us with the opportunity to propose this paper and fortheir continued useful suggestions from various perspectives.

21

Appendix 1 List of Transparency Disclosure Items for AI Algorithms

Lv. 1
Product
concept

Lv. 2
Purpose of use
Benefit/Impact

Main entry(indictionary) System Data
3.4.1
〇.
3.4.2
〇.

Model Output

Human Disclosure Items
Purpose of using AI〇.

Assessment of the benefits and impact of using AI
Processes where AI is used, and the role AI plays (e.g.,does it make decisions itself or does it support humandecision-making?)
Department/person responsible for development andoperation
Diversity of the development team and alignment withthe expected users〇.

Management system and methods of outsourcing

3.4.3
AI integration
Developmen Development manager
t teamand operation manager
DEIB Policy ofdevelopment teamOutsourcing
management
Overview of training
Data
data
Sources of training data

〇.
3.5.1

〇.

3.5.2
3.5.3
3.1.1

〇.

Overview of training and test data

3.1.2

〇.

Location of training and test data

3.1.3

〇.

How and where to obtain training and test data
Diversity of training and test data, consistency withexpected users
Overview of the actual data used (what does AI actually
use as Data to extract Outputs)
Phases of algorithms used (research, testing,production, etc.)
Learning methods of AI (expert systems, rule-based,machine learning, deep learning, etc.)

Training data collection
DEIB policy of training 3.1.4
data
3.1.5
Actual operational data
3.2.1
AI algorithm Utilization Phase
3.2.2
Learning Methods
3.2.3
Explainability
Parameters/features and 3.2.4
their purpose/reasons

〇.
〇.
〇.
〇.
〇.

Explainability of the algorithm
Parameters abstracted and verbalized to a reasonably
understandable degree〇.
22Appendix 1 List of Transparency Disclosure Items for AI Algorithms

Weight and its
purpose/reason
Different treatment foreach user category,including billing

3.2.5

Weight for main parameters and why they are relatively
important compared to other parameters
Relationship between parameters, weighting and userclassification such as actions and attributes including
billing
Due process for changing the algorithm, impact of thechange, purpose of the change, etc.
Significant probability of output, error rates, false
negatives/false positives, etc.

〇.

3.2.6

3.2.7
Due process for change
Performance
3.3.1
Outputaccuracy/limitations
Monitoring and
3.4.4
maintenance by
operators
Control by
3.4.4.1
user
Choice of algorithm use
3.4.4.2
Feedback
3.4.4.3
Data opt-out
Management
3.5.5
structure
Risk management
3.5.6
Educational system
3.5.7
Audit system
3.5.8

〇.
〇.
〇.

〇.
〇.
〇.
〇.
〇.
〇.
〇.
〇.

Inquiry response

23

Monitoring and maintenance system for output quality(frequency, method, and system), number of detections
The right to choose whether or not to accept thealgorithm
Availability, acceptability, and method of feedback on
algorithms and datasets by users
Availability of opt-out method from training data
Assessment, minimization, or management response to
the risks of using AI
Status of education related to the development andprovision of AI
Audit system and implementation status for thedevelopment and provision of AI
Contact person/appeal office, customer experiencesystem (frequency, methods, and structure), number ofcomplaints after a certain period of time

Appendix 2: Use Case 1: Government agencies use AI to review applications for grant programs

Lv1
Product
concept

Lv2
Purpose of use

Benefit/Impact

AI integration
Developmen Development manager
t teamand operation manager
DEIB Policy ofdevelopment teamOutsourcing
management
Data

Overview of training
data

Disclosure Example
The department in charge of reviewing grant applications will use an AI algorithm (the "AI") to scrutinize a large
number of review documents and identify those applications that do not meet the screening criteria.
Benefit to the department in charge of examination:
The department plans to conduct 2.5 million examinations per month, and the use of this AI will reduce the manhours required for examination by 70%. Therefore, the number of examination days will be shortened byapproximately one week and the monthly labor cost will be reduced by approximately 420 million JPY comparedto the case where all examinations are conducted by humans.
Benefit to the applicants:
The use of the AI will shorten the time to receive the grant by approximately one week.
The AI will be used in the following examinations. However, in all examinations, the final decision is made by theexaminer, and the role of the AI is only supplementary sorting for that decision.
Formal examination of documents:
Formal aspects such as erroneous or unclear statements are examined.
Formal screening of eligibility criteria:
The applicant will be screened for eligibility for the grant based on the business income, type of industry, etc. asfilled in the documents.
Substantive review of eligibility criteria:
Priority of grant recipients to be distributed within a budget cap is reviewed from a variety of data (see below).
CTO of XX Agency (concurrently serving as head of Information Systems Division): XX
Recognizing the diversity of applicant demographics (particularly age, gender and industry), XX Company, thedeveloper of the AI, has established a process during the testing phase whereby subjects of each demographic(those conducting the pilot application) are checked for the occurrence of discriminatory results.
We require XX, the developer of the AI, to comply with the development guidelines set forth by Japan and, inparticular, require reports on the following items:
If the results are unexplainable or extremely difficult to explain
If there is any doubt about the accuracy of the AI's judgment
XX.
The data used to develop the AI in this case were past grant application documents held by the Agency, pastreview results and reasons, government statistical data including earnings by year for each industry, data related to
tax returns, and data related to tax payments.
24Appendix 2: Use Case 1: Government agencies use AI to review applications for grant programs

Sources of training data (This is sensitive personal information and the data cannot be disclosed.)Training data collection Data submitted directly by the individual to the Agency or to another agency that holds the data.
As mentioned above, we use data from past grant applications of the same purpose as training data, but we exclude
DEIB Policy of training from the data set any outlier data (e.g., industries that were explicitly excluded) that we consider not to constitutedata
the population of the current study.
Actual operational data
AI algorithm Utilization phase

The data submitted by the applicant for this grant application will be used in the review process.
The AI is already in the production phase.

Learning methods

Machine learning is the main method, but there are some rule-based aspects of formal screening.
Basically, the results output by the AI can be explained by the following parameters, features, and weight.
However, there are extremely rare cases that are difficult to explain, in which case the AI output results will not be
Explainability
used and the examiner will make a decision on a case-by-case basis.
In particular, the substantive examination of the subject criteria has the following as key parameters/features:
Positive results are more likely to be generated for industries that are considered to have experienced adeterioration in earnings this fiscal year based on multiple government statistics, etc.
Age and gender (in light of past screening results, positive results may occur as attributes that may beparticularly protective due to their relationship to the industry and earnings situation. For more information,please click here.)
The current year's earnings (positive results are more likely to be generated if the average of the current year's
earnings is lower than the average of the previous five fiscal years).
Amended tax return history for the past three fiscal years
Parameters/features andTax payment status for the past 10 fiscal years (negative results are likely to be generated only if there are
their purpose/reasonsdelinquent taxes or additional taxes due).
Weight and its
purpose/reason
Industry and the current year's earnings situation are the most important factors.
Different treatment foreach user category,including billing
N/A

OutputDue process for change
Performanceaccuracy/limitations

There are no plans to change the algorithm of this AI within the fiscal year.
When the AI results were compared to the past review documents and review results, it was 97% accurate. As forthe remaining 3%, it can be said that many of the applications contain outliers or there are doubts about the validity
25

Appendix 2: Use Case 1: Government agencies use AI to review applications for grant programsof the past examination results. Therefore, multiple examiners will review the results of the AI, and the final
decision will be made by humans.
Monitoring andmaintenance by
operators
Control byuser

The results of the AI will be reviewed alternately by two reviewers, and if both reviewers find that they disagreewith the AI, the results will be feedback to XX Company, the developer of the AI, for use in improving the AI.

Choice of algorithm use None, due to the nature of the usage of this AI.
FeedbackNone, due to the nature of the usage of this AI.

Data opt-out

None, due to the nature of the usage of this AI.
There is a risk that the AI in this case will output inappropriate screening results, and this is addressed below:
Review and feedback by multiple reviewers
Check for consistency with past review results
Feedback on appeals by the applicant and any errors in the AI that are revealed by such appeals.
The reviewers have received a total of 15 hours of training in the specifications and use of the AI and have passed
the standard test required by the Agency.
The Agency's audit department will conduct an audit of this year's grant application decisions within one year, andany doubts about the AI or the reviewing officer's decisions may be made public and will be feedback to Company
XX, the developer of this AI.
Inquiries or appeals regarding this AI or the grant application should be directed to
XX Agency XX Grant Application Inquiry Office
Phone number: XX
E-mail address: XX

Management
structure
Risk management
Educational system
Audit system

Inquiry response

26

Appendix 3: Use Case 2: Using AI Recommendation Functions for Social Media Feeds

Lv1
Product
concept

Lv2
Purpose of use

Disclosure Example
Recommendation AI (the "AI") will be used within the social media "YY" post feed to provide users with content
that is highly convenient and relevant to them, rather than displaying content in the order of breaking news.

Benefit/Impact

Users receive more satisfying content and spend their limited time optimally.

AI integration
Developmen Development manager
t teamand operation manager
DEIB Policy ofdevelopment teamOutsourcing
management
Overview of training
Data
data

The development team is guaranteed to be diverse in terms of race, gender, religion, and education, and at least
one person is required to review the development of this AI from a minority perspective.

The training data includes the past 3 years of posts in YY, user reactions to the posts (reaction buttons, comments,
diffusion, viewing time, etc.), and post formats (text, image, video).
Sample datasets are available from the following repositories:
Sources of training data YY.com/repository/feed/sample-dataset/Training data collection Feed in YY
Since the data is obtained from YY internal feeds, a certain degree of representativeness of YY users is ensured.
DEIB Policy of training For example, users who stopped using the system after the first startup or users who behave like bots are excluded
datafrom the population of the dataset as outliers.
Actual operational data

Actual post data by the user is used for actual operation.

AI algorithm Utilization phase-

Learning methods-

Explainability

The AI in this case mainly holds the following parameters. In addition, if you select "Why is this post displayed?"in the detail menu of each individual post, the reason for displaying the individual post will be explained alongwith the parameters used.
·
Relationships among users·
Amount of common friends among users
Parameters/features andtheir purpose/reasons·Content of user interactions (e.g., mutual reactions)27

Appendix 3: Use Case 2: Using AI Recommendation Functions for Social Media Feeds

Weight and its
purpose/reason
Different treatment foreach user category,including billing

OutputControl byuser

Due process for change
Performanceaccuracy/limitations
Monitoring andmaintenance by
operators·
Amount of messages between users·
Time spent by one user on the other user's post·
User classification·
Post Content·
Format (text, images, video)·
Being current·
Relevance·
Regional character·
Reactions to the post·
Amount of reactions·
Frequency of reactions·
Degree of reaction cross-community
The parameters of relative importance as of X month 2023 are the amount of relationships and reactions amongusers and the degree of cross-community.
Payed users' posts are preferentially displayed more often and at a higher frequency than non-payed users' posts.
For material changes to the AI, we will provide at least 7 days' notice to the user. For adverse changes related to
payed users, we will provide a reasonable period of time to cancel the recurring payments.

To ensure that the AI does not handle too much illegal and harmful information, our Trust & Safety team regularly
monitors the feeds and removes or down-ranks the posts in question. The nature and number of these actions are
published in our annual report each fiscal year.
If you would like to enjoy YY feeds in the order of breaking news, not in the order of recommendation by the AI,
please select "Settings" →"Change Display Order" →"View in Order of Breaking News." Likewise, you can
Choice of algorithm use always enjoy the feeds in the order recommended by the AI from the same setting.
If you find a post in YY's feed that you are not satisfied with, please give feedback to the AI by long-pressing thepost or clicking the "..." button below the post and then clicking the "I am not interested in such post" button. You
can also select a reason on the next screen to make a more personalized feedback. Please note, however, that there
Feedback
is a time lag before the AI can make good use of your feedback.
Data opt-out

To exercise your rights regarding your personal data, please go to "Settings" →"About Personal Data."

28

Appendix 3: Use Case 2: Using AI Recommendation Functions for Social Media Feeds

Management
structure
Risk management-

Educational system-

Audit system

For inquiries about this AI, please contact
support-recommendation@example.com

Inquiry response

29

Appendix 4: Comparative Table of Regulatory Status in Each Country and this Toolkit

GDPR
(EU)
Lv1
Product
concept

Lv2
〇

AI
P2B
DSA
Regulation Regulation (EU)
(EU)
(EU)
〇
〇
〇

DMA
(EU)

ATRS
(UK)

AAA
(US)

TFDPA
(JP)

〇

Purpose of use
Benefit/Impact

AI integration
Developmen Development manager
t teamand operation manager
DEIB Policy ofdevelopment teamOutsourcing
management
Overview of training
Data
data

〇

〇

〇

〇

〇

〇

〇

〇
〇

〇
〇

Sources of training data
Training data collection
DEIB Policy of training
data

〇

〇
〇

〇

〇
〇

Actual operational data 〇〇〇AI algorithm Utilization phase〇

Learning methods〇

Explainability
Parameters/features andtheir purpose/reasons
Weight and its
purpose/reason

〇

〇

〇

〇

〇

〇

〇

〇

〇

30

Appendix 4: Comparative Table of Regulatory Status in Each Country and this Toolkit

OutputControl byuser

Different treatment foreach user category,including billing〇

Due process for change
Performanceaccuracy/limitations
Monitoring andmaintenance by
operators

〇

〇

〇

〇

〇
〇

〇

〇

〇

〇

〇

〇

〇

〇

〇

Choice of algorithm use〇

Feedback
Data opt-out

〇

〇

〇

Management
structure
Risk management〇〇

Educational system〇

Audit system

〇

〇

〇

〇

〇

〇

〇

〇

〇

〇

〇
〇
〇
〇
Inquiry response*GDPR: General Data Protection Regulation *AI Regulation: AI Regulation *P2B Regulation: The EU Regulation on platform-to-business relations*DSA: Digital Services Act *DMA: Digital Market Act *ATRS: Algorithmic Transparency Recording Standard*AAA: Algorithmic Accountability Act *DPF Transparency Act: Act on Enhancing Transparency and Fairness of Certain Digital Platforms

31