RCModel, a Risk Chain Model for Risk Reduction in AI Services

Technology, Governance Policy Research Unit  Institute for Future Initiatives  The University of Tokyo

WEKO3

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RCModel, a Risk Chain Model for Risk Reduction in AI Services

http://hdl.handle.net/2261/00079367

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policy_recommendation_tg_20200706.pdf (666.4 kB)

Item type

テクニカルレポート / Technical Report(1)

公開日

2020-07-06

タイトル

RCModel, a Risk Chain Model for Risk Reduction in AI Services

言語

eng

資源タイプ

資源

http://purl.org/coar/resource_type/c_18gh

タイプ

technical report

その他のタイトル

AIサービスのリスク低減を検討するリスクチェーンモデルの提案

著者

Technology, Governance Policy Research Unit Institute for Future Initiatives The University of Tokyo

WEKO 160902

	Technology, Governance Policy Research Unit Institute for Future Initiatives The University of Tokyo

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著者別名

識別子Scheme

WEKO

識別子

160903

姓名

東京大学未来ビジョン研究センター技術ガバナンス研究ユニット

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内容記述タイプ

Abstract

内容記述

With the increasing use of artificial intelligence (AI) services and products in recent years, issues related to their trustworthiness have emerged and AI service providers need to be prepared for various risks. In this policy recommendation, we propose a risk chain model (RCModel) that supports AI service providers in proper risk assessment and control. We hope that RCModel will contribute to the realization of trustworthy AI services.

Overview of RCModel
1)Organization and structure of risk components
There are a number of potential risk factors involved in provision of AI services. In RCModel, these factors are segregated into (1) technical components of the AI system, (2) components related to the code of conduct (including communication with users) of the service provider, and (3) components related to the user’s understanding, behavior, and usage environment.

2)Identification of risk scenarios and risk-contributing factors
RCModel helps identify risk scenarios related to AI services, such as unfair decisions and uncontrollable accidents. It then identifies risk factors for priority risk scenarios.

3)Visualization of risk chains and planning risk control
Because it is difficult to reduce risk sufficiently on a factor basis, AI service providers can consider stepwise risk reduction by visualizing the relationships (risk chain) among the risk factors related to risk scenarios. This allows consideration of where a risk exists and its effective and efficient control.

Policy Recommendations for Future Development and Implementation of AI Services Using RCModel
Policy Recommendation 1: Enhance understanding of risk scenarios and factors
Service providers need to properly understand the risk factors associated with their AI services. They should also pay attention to social incidents involving the use of AI technologies and recognize important risk scenarios.

Policy Recommendation 2：Promotion of appropriate risk controls using RCModel
AI service providers should formulate their risk control measures by analyzing RCModel’s risk chain. It is neither necessary nor always possible to reduce all the risks identified; therefore, appropriate controls should be established within an enterprise based on factors such as magnitude of risks posed, technical difficulty, cost-effectiveness, and continuity.

Policy Recommendation 3：Promoting and updating dialogue among stakeholders
RCModel should be used to facilitate dialogue among AI service providers, AI developers, and users. In addition, a system should be established to clarify risk tolerance, create risk scenarios, structure risk factors, examine risk control models, and create a common understanding on the scope of each stakeholder’s responsibility.

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発行日 2020-07-06

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CC BY 4.0

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Institute for Future Initiatives, The University of Tokyo

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2021-03-01 10:51:40.954635

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RCModel, a Risk Chain Model for Risk Reduction in AI Services

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