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

1. AI should reflect the diversity of the users it serves

Both industry and community must develop effective mechanisms to filter bias as well as negative sentiment in the data that AI learns from – ensuring AI does not perpetuate stereotypes.

Principle: The Ethics of Code: Developing AI for Business with Five Core Principles, Jun 27, 2017

Published by Sage

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

· (2) Education

In a society premised on AI, we have to eliminate disparities, divisions, or socially weak people. Therefore, policy makers and managers of the enterprises involved in AI must have an accurate understanding of AI, the knowledge for proper use of AI in society and AI ethics, taking into account the complexity of AI and the possibility that AI can be misused intentionally. The AI user should understand the outline of AI and be educated to utilize it properly because AI is much more complicated than the already developed conventional tools. On the other hand, from the viewpoint of AI’s contributions to society, it is important for the developers of AI to learn about the social sciences, business models, and ethics, including normative awareness of norms and wide range of liberal arts not to mention the basis possibly generated by AI. From the above point of view, it is necessary to establish an educational environment that provides AI literacy according to the following principles, equally to every person. In order to get rid of disparity between people having a good knowledge about AI technology and those being weak in it, opportunities for education such as AI literacy are widely provided in early childhood education and primary and secondary education. The opportunities of learning about AI should be provided for the elderly people as well as workforce generation. Our society needs an education scheme by which anyone should be able to learn AI, mathematics, and data science beyond the boundaries of literature and science. Literacy education provides the following contents: 1) Data used by AI are usually contaminated by bias, 2) AI is easy to generate unwanted bias in its use, and 3) The issues of impartiality, fairness, and privacy protection which are inherent to actual use of AI. In a society in which AI is widely used, the educational environment is expected to change from the current unilateral and uniform teaching style to one that matches the interests and skill level of each individual person. Therefore, the society probably shares the view that the education system will change constantly to the above mentioned education style, regardless of the success experience in the educational system of the past. In education, it is especially important to avoid dropouts. For this, it is desirable to introduce an interactive educational environment which fully utilizes AI technologies and allows students to work together to feel a kind accomplishment. In order to develop such an educational environment, it is desirable that companies and citizens work on their own initiative, not to burden administrations and schools (teachers).

Published by Cabinet Office, Government of Japan in Social Principles of Human-centric AI, Dec 27, 2018

1. Artificial intelligence should be developed for the common good and benefit of humanity.

The UK must seek to actively shape AI's development and utilisation, or risk passively acquiescing to its many likely consequences. A shared ethical AI framework is needed to give clarity as to how AI can best be used to benefit individuals and society. By establishing these principles, the UK can lead by example in the international community. We recommend that the Government convene a global summit of governments, academia and industry to establish international norms for the design, development, regulation and deployment of artificial intelligence. The prejudices of the past must not be unwittingly built into automated systems, and such systems must be carefully designed from the beginning, with input from as diverse a group of people as possible.

Published by House of Lords of United Kingdom, Select Committee on Artificial Intelligence in AI Code, Apr 16, 2018

· 1. THE KEY PRIORITY OF AI TECHNOLOGIES DEVELOPMENT IS PROTECTION OF THE INTERESTS AND RIGHTS OF HUMAN BEINGS AT LARGE AND EVERY PERSON IN PARTICULAR

1.1. Human centered and humanistic approach. Human rights and freedoms and the human as such must be treated as the greatest value in the process of AI technologies development. AI technologies developed by Actors should promote or not hinder the full realization of all human capabilities to achieve harmony in social, economic and spiritual spheres, as well as the highest self fulfillment of human beings. AI Actors should regard core values such as the preservation and development of human cognitive abilities and creative potential; the preservation of moral, spiritual and cultural values; the promotion of cultural and linguistic diversity and identity; and the preservation of traditions and the foundations of nations, peoples, ethnic and social groups. A human centered and humanistic approach is the basic ethical principle and central criterion for assessing the ethical behavior of AI Actors listed in Section 2 of this Code. 1.2. Recognition of autonomy and free will of human. AI Actors should take necessary measures to preserve the autonomy and free will of human in the process of decision making, their right to choose, as well as preserve human intellectual abilities in general as an intrinsic value and a system forming factor of modern civilization. AI Actors should forecast possible negative consequences for the development of human cognitive abilities at the earliest stages of AI systems creation and refrain from the development of AI systems that purposefully cause such consequences. 1.3. Compliance with the law. AI Actors must know and comply with the provisions of the national legislation in all areas of their activities and at all stages of creation, integration and use of AI technologies, i.a. in the sphere of legal responsibility of AI Actors. 1.4. Non discrimination. To ensure fairness and non discrimination, AI Actors should take measures to verify that the algorithms, datasets and processing methods for machine learning that are used to group and or classify data that concern individuals or groups do not entail intentional discrimination. AI Actors are encouraged to create and apply methods and software solutions that identify and prevent discrimination manifestations based on race, nationality, gender, political views, religious beliefs, age, social and economic status, or information about private life (at the same time, the rules of functioning or application of AI systems for different groups of users wherein such factors are taken into account for user segmentation, which are explicitly declared by an AI Actor, cannot be defined as discrimination). 1.5. Assessment of risks and humanitarian impact. AI Actors are encouraged to: • assess the potential risks of the use of an AI system, including social consequences for individuals, society and the state, as well as the humanitarian impact of an AI system on human rights and freedoms at different stages of its life cycle, i.a. during the formation and use of datasets; • monitor the manifestations of such risks in the long term; • take into account the complexity of AI systems’ actions, including interconnection and interdependence of processes in the AI systems’ life cycle, during risk assessment. In special cases concerning critical applications of an AI system it is encouraged that risk assessment be conducted with the involvement of a neutral third party or authorized official body given that it does not harm the performance and information security of the AI system and ensures the protection of the intellectual property and trade secrets of the developer.

Published by AI Alliance Russia in AI Ethics Code (revised version), Oct 21, 2022 (unconfirmed)