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

· ⑧ Accountability

Responsible parties should be clearly defined during the process of AI development and utilization to minimize potential damage. Roles and responsibilities should be clearly defined among the designers, developers, service providers, and users of AI.

Principle: National AI Ethical Guidelines, Dec 23, 2020

Published by The Ministry of Science and ICT (MSIT) and the Korea Information Society Development Institute (KISDI)

Related Principles

6. Accountability and Integrity

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

VII. Accountability

Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes, both before and after their implementation. Auditability of AI systems is key in this regard, as the assessment of AI systems by internal and external auditors, and the availability of such evaluation reports, strongly contributes to the trustworthiness of the technology. External auditability should especially be ensured in applications affecting fundamental rights, including safety critical applications. Potential negative impacts of AI systems should be identified, assessed, documented and minimised. The use of impact assessments facilitates this process. These assessments should be proportionate to the extent of the risks that the AI systems pose. Trade offs between the requirements – which are often unavoidable – should be addressed in a rational and methodological manner, and should be accounted for. Finally, when unjust adverse impact occurs, accessible mechanisms should be foreseen that ensure adequate redress.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

6. Shared Responsibility

AI developers, users and other related stakeholders should have a high sense of social responsibility and self discipline, and should strictly abide by laws, regulations, ethical principles, technical standards and social norms. AI accountability mechanisms should be established to clarify the responsibilities of researchers, developers, users, and relevant parties. Users of AI products and services and other stakeholders should be informed of the potential risks and impacts in advance. Using AI for illegal activities should be strictly prohibited.

Published by National Governance Committee for the New Generation Artificial Intelligence, China in Governance Principles for the New Generation Artificial Intelligence--Developing Responsible Artificial Intelligence, Jun 17, 2019

Principle 7 – Accountability & Responsibility

The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities. Human oversight, governance, and proper management should be demonstrated across the entire AI System Lifecycle to ensure that proper mechanisms are in place to avoid harm and misuse of this technology. AI systems should never lead to people being deceived or unjustifiably impaired in their freedom of choice. The designers, developers, and people who implement the AI system should be identifiable and assume responsibility and accountability for any potential damage the technology has on individuals or communities, even if the adverse impact is unintended. The liable parties should take necessary preventive actions as well as set risk assessment and mitigation strategy to minimize the harm due to the AI system. The accountability and responsibility principle is closely related to the fairness principle. The parties responsible for the AI system should ensure that the fairness of the system is maintained and sustained through control mechanisms. All parties involved in the AI System Lifecycle should consider and action these values in their decisions and execution.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

4 Foster responsibility and accountability

Humans require clear, transparent specification of the tasks that systems can perform and the conditions under which they can achieve the desired level of performance; this helps to ensure that health care providers can use an AI technology responsibly. Although AI technologies perform specific tasks, it is the responsibility of human stakeholders to ensure that they can perform those tasks and that they are used under appropriate conditions. Responsibility can be assured by application of “human warranty”, which implies evaluation by patients and clinicians in the development and deployment of AI technologies. In human warranty, regulatory principles are applied upstream and downstream of the algorithm by establishing points of human supervision. The critical points of supervision are identified by discussions among professionals, patients and designers. The goal is to ensure that the algorithm remains on a machine learning development path that is medically effective, can be interrogated and is ethically responsible; it involves active partnership with patients and the public, such as meaningful public consultation and debate (101). Ultimately, such work should be validated by regulatory agencies or other supervisory authorities. When something does go wrong in application of an AI technology, there should be accountability. Appropriate mechanisms should be adopted to ensure questioning by and redress for individuals and groups adversely affected by algorithmically informed decisions. This should include access to prompt, effective remedies and redress from governments and companies that deploy AI technologies for health care. Redress should include compensation, rehabilitation, restitution, sanctions where necessary and a guarantee of non repetition. The use of AI technologies in medicine requires attribution of responsibility within complex systems in which responsibility is distributed among numerous agents. When medical decisions by AI technologies harm individuals, responsibility and accountability processes should clearly identify the relative roles of manufacturers and clinical users in the harm. This is an evolving challenge and remains unsettled in the laws of most countries. Institutions have not only legal liability but also a duty to assume responsibility for decisions made by the algorithms they use, even if it is not feasible to explain in detail how the algorithms produce their results. To avoid diffusion of responsibility, in which “everybody’s problem becomes nobody’s responsibility”, a faultless responsibility model (“collective responsibility”), in which all the agents involved in the development and deployment of an AI technology are held responsible, can encourage all actors to act with integrity and minimize harm. In such a model, the actual intentions of each agent (or actor) or their ability to control an outcome are not considered.

Published by World Health Organization (WHO) in Key ethical principles for use of artificial intelligence for health, Jun 28, 2021