Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

6. Pursuit of Transparency

During the planning and design stages for its products and services that utilize AI, Sony will strive to introduce methods of capturing the reasoning behind the decisions made by AI utilized in said products and services. Additionally, it will endeavor to provide intelligible explanations and information to customers about the possible impact of using these products and services.

Principle: Sony Group AI Ethics Guidelines, Sep 25, 2018

Published by Sony Group

Related Principles

1. Transparency and Explainability

Transparency refers to providing disclosure on when an AI system is being used and the involvement of an AI system in decision making, what kind of data it uses, and its purpose. By disclosing to individuals that AI is used in the system, individuals will become aware and can make an informed choice of whether to use the AIenabled system. Explainability is the ability to communicate the reasoning behind an AI system’s decision in a way that is understandable to a range of people, as it is not always clear how an AI system has arrived at a conclusion. This allows individuals to know the factors contributing to the AI system’s recommendation. In order to build public trust in AI, it is important to ensure that users are aware of the use of AI technology and understand how information from their interaction is used and how the AI system makes its decisions using the information provided. In line with the principle of transparency, deployers have a responsibility to clearly disclose the implementation of an AI system to stakeholders and foster general awareness of the AI system being used. With the increasing use of AI in many businesses and industries, the public is becoming more aware and interested in knowing when they are interacting with AI systems. Knowing when and how AI systems interact with users is also important in helping users discern the potential harm of interacting with an AI system that is not behaving as intended. In the past, AI algorithms have been found to discriminate against female job applicants and have failed to accurately recognise the faces of dark skinned women. It is important for users to be aware of the expected behaviour of the AI systems so they can make more informed decisions about the potential harm of interacting with AI systems. An example of transparency in an AI enabled ecommerce platform is informing users that their purchase history is used by the platform’s recommendation algorithm to identify similar products and display them on the users’ feeds. In line with the principle of explainability, developers and deployers designing, developing, and deploying AI systems should also strive to foster general understanding among users of how such systems work with simple and easy to understand explanations on how the AI system makes decisions. Understanding how AI systems work will help humans know when to trust its decisions. Explanations can have varying degrees of complexity, ranging from a simple text explanation of which factors more significantly affected the decisionmaking process to displaying a heatmap over the relevant text or on the area of an image that led to the system’s decision. For example, when an AI system is used to predict the likelihood of cardiac arrest in patients, explainability can be implemented by informing medical professionals of the most significant factors (e.g., age, blood pressure, etc.) that influenced the AI system’s decision so that they can subsequently make informed decisions on their own. Where “black box” models are deployed, rendering it difficult, if not impossible to provide explanations as to the workings of the AI system, outcome based explanations, with a focus on explaining the impact of decisionmaking or results flowing from the AI system may be relied on. Alternatively, deployers may also consider focusing on aspects relating to the quality of the AI system or preparing information that could build user confidence in the outcomes of an AI system’s processing behaviour. Some of these measures are: • Documenting the repeatability of results produced by the AI system. Some practices to demonstrate repeatability include conducting repeatability assessments to ensure deployments in live environments are repeatable and performing counterfactual fairness testing to ensure that the AI system’s decisions are the same in both the real world and in the counterfactual world. Repeatability refers to the ability of the system to consistently obtain the same results, given the same scenario. Repeatability often applies within the same environment, with the same data and the same computational conditions. • Ensuring traceability by building an audit trail to document the AI system development and decisionmaking process, implementing a black box recorder that captures all input data streams, or storing data appropriately to avoid degradation and alteration. • Facilitating auditability by keeping a comprehensive record of data provenance, procurement, preprocessing, lineage, storage, and security. Such information can also be centralised digitally in a process log to increase capacity to cater the presentation of results to different tiers of stakeholders with different interests and levels of expertise. Deployers should, however, note that auditability does not necessarily entail making certain confidential information about business models or intellectual property related to the AI system publicly available. A risk based approach can be taken towards identifying the subset of AI enabled features in the AI system for which implemented auditability is necessary to align with regulatory requirements or industry practices. • Using AI Model Cards, which are short documents accompanying trained machine learning models that disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information. In cases where AI systems are procured directly from developers, deployers will have to work together with these developers to achieve transparency. More on this will be covered in later sections of the Guide.

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

9. Principle of accountability

Developers should make efforts to fulfill their accountability to stakeholders, including AI systems’ users. [Comment] Developers are expected to fulfill their accountability for AI systems they have developed to gain users’ trust in AI systems. Specifically, it is encouraged that developers make efforts to provide users with the information that can help their choice and utilization of AI systems. In addition, in order to improve the acceptance of AI systems by the society including users, it is also encouraged that, taking into account the R&D principles (1) to (8) set forth in the Guidelines, developers make efforts: (a) to provide users et al. with both information and explanations about the technical characteristics of the AI systems they have developed; and (b) to gain active involvement of stakeholders (such as their feedback) in such manners as to hear various views through dialogues with diverse stakeholders. Moreover, it is advisable that developers make efforts to share the information and cooperate with providers et al. who offer services with the AI systems they have developed on their own.

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in AI R&D Principles, Jul 28, 2017

10.Principle of accountability

AI service providers and business users should make efforts to fulfill their accountability to the stakeholders including consumer users and indirect users. [Main points to discuss] A) Efforts to fulfill accountability In light of the characteristics of AI to be used and its purpose, etc., AI service providers and business users may be expected to make efforts to establish appropriate accountability to consumer users, indirect users, and third parties affected by the use of AI, to gain enough trust in AI from people and society. B) Notification and publication of usage policy on AI systems or AI services AI service providers and business users may be expected to notify or announce the usage policy on AI (the fact that they provide AI services, the scope and manner of proper AI utilization, the risks associated with the utilization, and the establishment of a consultation desk) in order to enable consumer users and indirect users to recognize properly the usage of AI. In light of the characteristics of the technologies to be used and their usage, we have to focus on which cases will lead to the usage policy is expected to be notified or announced as well as what content is expected to be included in the usage policy.

Published by Ministry of Internal Affairs and Communications (MIC), the Government of Japan in Draft AI Utilization Principles, Jul 17, 2018

3. Provision of Trusted Products and Services

Sony understands the need for safety when dealing with products and services utilizing AI and will continue to respond to security risks such as unauthorized access. AI systems may utilize statistical or probabilistic methods to achieve results. In the interest of Sony’s customers and to maintain their trust, Sony will design whole systems with an awareness of the responsibility associated with the characteristics of such methods.

Published by Sony Group in Sony Group AI Ethics Guidelines, Sep 25, 2018

· ⑩ Transparency

In order to build social trust, efforts should be made, while taking into account possible conflicts with other principles, to improve the transparency and explainability of AI to a level suitable for the use cases of the AI system. When providing AI powered products or services, the AI provider should inform users in advance about what the AI does and what risks may arise during its use.

Published by The Ministry of Science and ICT (MSIT) and the Korea Information Society Development Institute (KISDI) in National AI Ethical Guidelines, Dec 23, 2020