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

· 8) Judicial Transparency

Any involvement by an autonomous system in judicial decision making should provide a satisfactory explanation auditable by a competent human authority.

Principle: Asilomar AI Principles, Jan 3-8, 2017

Published by Future of Life Institute (FLI), Beneficial AI 2017

Related Principles

(f) Rule of law and accountability

Rule of law, access to justice and the right to redress and a fair trial provide the necessary framework for ensuring the observance of human rights standards and potential AI specific regulations. This includes protections against risks stemming from ‘autonomous’ systems that could infringe human rights, such as safety and privacy. The whole range of legal challenges arising in the field should be addressed with timely investment in the development of robust solutions that provide a fair and clear allocation of responsibilities and efficient mechanisms of binding law. In this regard, governments and international organisations ought to increase their efforts in clarifying with whom liabilities lie for damages caused by undesired behaviour of ‘autonomous’ systems. Moreover, effective harm mitigation systems should be in place.

Published by European Group on Ethics in Science and New Technologies, European Commission in Ethical principles and democratic prerequisites, Mar 9, 2018

· 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

· 1.5 Liability of AI Systems Due to Autonomy

The use of AI to make autonomous consequential decisions about people, informed by – but often replacing decisions made by – human driven bureaucratic processes, has led to concerns about liability. Acknowledging existing legal and regulatory frameworks, we are committed to partnering with relevant stakeholders to inform a reasonable accountability framework for all entities in the context of autonomous systems.

Published by Information Technology Industry Council (ITI) in AI Policy Principles, Oct 24, 2017

3. Scientific Integrity and Information Quality

The government’s regulatory and non regulatory approaches to AI applications should leverage scientific and technical information and processes. Agencies should hold information, whether produced by the government or acquired by the government from third parties, that is likely to have a clear and substantial influence on important public policy or private sector decisions (including those made by consumers) to a high standard of quality, transparency, and compliance. Consistent with the principles of scientific integrity in the rulemaking and guidance processes, agencies should develop regulatory approaches to AI in a manner that both informs policy decisions and fosters public trust in AI. Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results. Agencies should also be mindful that, for AI applications to produce predictable, reliable, and optimized outcomes, data used to train the AI system must be of sufficient quality for the intended use.

Published by The White House Office of Science and Technology Policy (OSTP), United States in Principles for the Stewardship of AI Applications, Nov 17, 2020