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

3. Traceable

The department's AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes and operational methods applicable to AI capabilities, including with transparent and auditable methodologies, data sources and design procedures and documentation.

Principle: DoD's AI ethical principles, Feb 24, 2020

Published by Department of Defense (DoD), United States

Related Principles

3. Traceable.

DoD’s AI engineering discipline should be sufficiently advanced such that technical experts possess an appropriate understanding of the technology, development processes, and operational methods of its AI systems, including transparent and auditable methodologies, data sources, and design procedure and documentation.

Published by Defense Innovation Board (DIB), Department of Defense (DoD), United States in AI Ethics Principles for DoD, Oct 31, 2019

C. Explainability and Traceability:

AI applications will be appropriately understandable and transparent, including through the use of review methodologies, sources, and procedures. This includes verification, assessment and validation mechanisms at either a NATO and or national level.

Published by The North Atlantic Treaty Organization (NATO) in NATO Principles of Responsible Use of Artificial Intelligence in Defence, Oct 22, 2021

· 6. MAXIMUM TRANSPARENCY AND RELIABILITY OF INFORMATION CONCERNING THE LEVEL OF AI TECHNOLOGIES DEVELOPMENT, THEIR CAPABILITIES AND RISKS ARE CRUCIAL

6.1. Reliability of information about AI systems. AI Actors are encouraged to provide AI systems users with reliable information about the AI systems and most effective methods of their use, harms, benefits acceptable areas and existing limitations of their use. 6.2. Awareness raising in the field of ethical AI application. AI Actors are encouraged to carry out activities aimed at increasing the level of trust and awareness of the citizens who use AI systems and the society at large, in the field of technologies being developed, the specifics of ethical use of AI systems and other issues related to AI systems development by all available means, i.a. by working on scientific and journalistic publications, organizing scientific and public conferences or seminars, as well as by adding the provisions about ethical behavior to the rules of AI systems operation for users and (or) operators, etc.

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

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