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

Definitions in the Guidelines

“AI” means any functionality or its enabling technology that performs information processing for various purposes that people perceive as intelligent, and that is embodied by machine learning based on data, or by rules or knowledge extracted in some methods. “Sony” means Sony Corporation and any company where more than 50% of voting rights are directly or indirectly owned by Sony Corporation.

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

4. Human centricity

AI systems should respect human centred values and pursue benefits for human society, including human beings’ well being, nutrition, happiness, etc. It is key to ensure that people benefit from AI design, development, and deployment while being protected from potential harms. AI systems should be used to promote human well being and ensure benefit for all. Especially in instances where AI systems are used to make decisions about humans or aid them, it is imperative that these systems are designed with human benefit in mind and do not take advantage of vulnerable individuals. Human centricity should be incorporated throughout the AI system lifecycle, starting from the design to development and deployment. Actions must be taken to understand the way users interact with the AI system, how it is perceived, and if there are any negative outcomes arising from its outputs. One example of how deployers can do this is to test the AI system with a small group of internal users from varied backgrounds and demographics and incorporate their feedback in the AI system. AI systems should not be used for malicious purposes or to sway or deceive users into making decisions that are not beneficial to them or society. In this regard, developers and deployers (if developing or designing inhouse) should also ensure that dark patterns are avoided. Dark patterns refer to the use of certain design techniques to manipulate users and trick them into making decisions that they would otherwise not have made. An example of a dark pattern is employing the use of default options that do not consider the end user’s interests, such as for data sharing and tracking of the user’s other online activities. As an extension of human centricity as a principle, it is also important to ensure that the adoption of AI systems and their deployment at scale do not unduly disrupt labour and job prospects without proper assessment. Deployers are encouraged to take up impact assessments to ensure a systematic and stakeholder based review and consider how jobs can be redesigned to incorporate use of AI. Personal Data Protection Commission of Singapore’s (PDPC) Guide on Job Redesign in the Age of AI6 provides useful guidance to assist organisations in considering the impact of AI on its employees, and how work tasks can be redesigned to help employees embrace AI and move towards higher value tasks.

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

· 5. The Principle of Explicability: “Operate transparently”

Transparency is key to building and maintaining citizen’s trust in the developers of AI systems and AI systems themselves. Both technological and business model transparency matter from an ethical standpoint. Technological transparency implies that AI systems be auditable, comprehensible and intelligible by human beings at varying levels of comprehension and expertise. Business model transparency means that human beings are knowingly informed of the intention of developers and technology implementers of AI systems. Explicability is a precondition for achieving informed consent from individuals interacting with AI systems and in order to ensure that the principle of explicability and non maleficence are achieved the requirement of informed consent should be sought. Explicability also requires accountability measures be put in place. Individuals and groups may request evidence of the baseline parameters and instructions given as inputs for AI decision making (the discovery or prediction sought by an AI system or the factors involved in the discovery or prediction made) by the organisations and developers of an AI system, the technology implementers, or another party in the supply chain.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

· 5. Non Discrimination

Discrimination concerns the variability of AI results between individuals or groups of people based on the exploitation of differences in their characteristics that can be considered either intentionally or unintentionally (such as ethnicity, gender, sexual orientation or age), which may negatively impact such individuals or groups. Direct or indirect discrimination through the use of AI can serve to exploit prejudice and marginalise certain groups. Those in control of algorithms may intentionally try to achieve unfair, discriminatory, or biased outcomes in order to exclude certain groups of persons. Intentional harm can, for instance, be achieved by explicit manipulation of the data to exclude certain groups. Harm may also result from exploitation of consumer biases or unfair competition, such as homogenisation of prices by means of collusion or non transparent market. Discrimination in an AI context can occur unintentionally due to, for example, problems with data such as bias, incompleteness and bad governance models. Machine learning algorithms identify patterns or regularities in data, and will therefore also follow the patterns resulting from biased and or incomplete data sets. An incomplete data set may not reflect the target group it is intended to represent. While it might be possible to remove clearly identifiable and unwanted bias when collecting data, data always carries some kind of bias. Therefore, the upstream identification of possible bias, which later can be rectified, is important to build in to the development of AI. Moreover, it is important to acknowledge that AI technology can be employed to identify this inherent bias, and hence to support awareness training on our own inherent bias. Accordingly, it can also assist us in making less biased decisions.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

· 8. Robustness

Trustworthy AI requires that algorithms are secure, reliable as well as robust enough to deal with errors or inconsistencies during the design, development, execution, deployment and use phase of the AI system, and to adequately cope with erroneous outcomes. Reliability & Reproducibility. Trustworthiness requires that the accuracy of results can be confirmed and reproduced by independent evaluation. However, the complexity, non determinism and opacity of many AI systems, together with sensitivity to training model building conditions, can make it difficult to reproduce results. Currently there is an increased awareness within the AI research community that reproducibility is a critical requirement in the field. Reproducibility is essential to guarantee that results are consistent across different situations, computational frameworks and input data. The lack of reproducibility can lead to unintended discrimination in AI decisions. Accuracy. Accuracy pertains to an AI’s confidence and ability to correctly classify information into the correct categories, or its ability to make correct predictions, recommendations, or decisions based on data or models. An explicit and well formed development and evaluation process can support, mitigate and correct unintended risks. Resilience to Attack. AI systems, like all software systems, can include vulnerabilities that can allow them to be exploited by adversaries. Hacking is an important case of intentional harm, by which the system will purposefully follow a different course of action than its original purpose. If an AI system is attacked, the data as well as system behaviour can be changed, leading the system to make different decisions, or causing the system to shut down altogether. Systems and or data can also become corrupted, by malicious intention or by exposure to unexpected situations. Poor governance, by which it becomes possible to intentionally or unintentionally tamper with the data, or grant access to the algorithms to unauthorised entities, can also result in discrimination, erroneous decisions, or even physical harm. Fall back plan. A secure AI has safeguards that enable a fall back plan in case of problems with the AI system. In some cases this can mean that the AI system switches from statistical to rule based procedure, in other cases it means that the system asks for a human operator before continuing the action.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018