There needs to be human accountability and control in the design, development, and deployment of AI systems. Deployers should be accountable for decisions made by AI systems and for the compliance with applicable laws and respect for AI ethics and principles. AI actors9 should act with integrity throughout the AI system lifecycle when designing, developing, and deploying AI systems. Deployers of AI systems should ensure the proper functioning of AI systems and its compliance with applicable laws, internal AI governance policies and ethical principles. In the event of a malfunction or misuse of the AI system that results in negative outcomes, responsible individuals should act with integrity and implement mitigating actions to prevent similar incidents from happening in the future. To facilitate the allocation of responsibilities, organisations should adopt clear reporting structures for internal governance, setting out clearly the different kinds of roles and responsibilities for those involved in the AI system lifecycle. AI systems should also be designed, developed, and deployed with integrity – any errors or unethical outcomes should at minimum be documented and corrected to prevent harm to users upon deployment

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

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

Published by Tencent Research Institute in "ARCC": An Ethical Framework for Artificial Intelligence, Sep 18, 2018

Published by Tencent Research Institute in "ARCC": An Ethical Framework for Artificial Intelligence, Sep 18, 2018

Published by Tencent Research Institute in "ARCC": An Ethical Framework for Artificial Intelligence, Sep 18, 2018