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

2. Transparency

Users will be aware that they are interacting with AI. AI will be explainable for users to understand its decision or recommendation to the extent technologically feasible. The process of collecting or utilizing personal data will be transparent.

Principle: Principles for AI Ethics, Apr 24, 2019 (unconfirmed)

Published by Samsung

Related Principles

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

3. New technology, including AI systems, must be transparent and explainable

For the public to trust AI, it must be transparent. Technology companies must be clear about who trains their AI systems, what data was used in that training and, most importantly, what went into their algorithm’s recommendations. If we are to use AI to help make important decisions, it must be explainable.

Published by IBM in Principles for Trust and Transparency, May 30, 2018

5 DEMOCRATIC PARTICIPATION PRINCIPLE

AIS must meet intelligibility, justiﬁability, and accessibility criteria, and must be subjected to democratic scrutiny, debate, and control. 1) AIS processes that make decisions affecting a person’s life, quality of life, or reputation must be intelligible to their creators. 2) The decisions made by AIS affecting a person’s life, quality of life, or reputation should always be justiﬁable in a language that is understood by the people who use them or who are subjected to the consequences of their use. Justiﬁcation consists in making transparent the most important factors and parameters shaping the decision, and should take the same form as the justiﬁcation we would demand of a human making the same kind of decision. 3) The code for algorithms, whether public or private, must always be accessible to the relevant public authorities and stakeholders for veriﬁcation and control purposes. 4) The discovery of AIS operating errors, unexpected or undesirable effects, security breaches, and data leaks must imperatively be reported to the relevant public authorities, stakeholders, and those affected by the situation. 5) In accordance with the transparency requirement for public decisions, the code for decision making algorithms used by public authorities must be accessible to all, with the exception of algorithms that present a high risk of serious danger if misused. 6) For public AIS that have a signiﬁcant impact on the life of citizens, citizens should have the opportunity and skills to deliberate on the social parameters of these AIS, their objectives, and the limits of their use. 7) We must at all times be able to verify that AIS are doing what they were programmed for and what they are used for. 8) Any person using a service should know if a decision concerning them or affecting them was made by an AIS. 9) Any user of a service employing chatbots should be able to easily identify whether they are interacting with an AIS or a real person. 10) Artiﬁcial intelligence research should remain open and accessible to all.

Published by University of Montreal in The Montreal Declaration for a Responsible Development of Artificial Intelligence, Dec 4, 2018

Transparency

Review mechanisms will ensure citizens can question and challenge AI based outcomes Not only must the people of NSW have high levels of assurance that data is being used safely and in accordance with relevant legislation, they must also have access to an efficient and transparent review mechanism if there are questions about the use of data or AI informed outcomes. The development of AI solutions must be robust technically, legally and ethically. The community should be engaged on the objectives of AI projectsand insights into data use and methodology should be made publicly available unless there is an overriding public interest in not doing so. Projects should clearly demonstrate: a publicly available project objective and planned outcomes how the public can question and seek reviews of AI based decisions how the community can get insights into data use and methodology how the community will be informed of changes to an AI solution, including where existing technology is adapted for another purpose.

Published by Government of New South Welsh, Australia in Mandatory Ethical Principles for the use of AI, 2024

Transparency and explainability

United Nations system organizations should ensure transparency and explainability of AI systems that they use at all stages of their lifecycle and of decision making processes involving AI systems. Technical explainability requires that the decisions made by an AI system can be understood and traced by human beings. Individuals should be meaningfully informed when a decision which may or will impact their rights, fundamental freedoms, entitlements, services or benefits, is informed by or made based on AI algorithms and have access to the reasons for a decision and the logic involved. The information and reasons for a decision should be presented in a manner that is understandable to them.

Published by United Nations System Chief Executives Board for Coordination in Principles for the Ethical Use of Artificial Intelligence in the United Nations System, Sept 20, 2022