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

5. Ethical and Practical Use of Data

o We have an active AI & Data Ethics Committee, comprised of both industry leaders and ADP experts, which meets on a regular cadence and reviews our design principles. The Committee advises on emerging industry trends and concerns and provides guidance with respect to the principles ADP should follow while developing products, systems and applications that involve AI and data.

Principle: ADP: Ethics in Artificial Intelligence, 2018 (unconfirmed)

Published by ADP

Related Principles

Ethical Considerations in Deployment and Design

Principle: AI system designers and builders need to apply a user centric approach to the technology. They need to consider their collective responsibility in building AI systems that will not pose security risks to the Internet and Internet users. Recommendations: Adopt ethical standards: Adherence to the principles and standards of ethical considerations in the design of artificial intelligence, should guide researchers and industry going forward. Promote ethical considerations in innovation policies: Innovation policies should require adherence to ethical standards as a pre requisite for things like funding.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 2017

· 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)

· Right to Privacy, and Data Protection

32. Privacy, a right essential to the protection of human dignity, human autonomy and human agency, must be respected, protected and promoted throughout the life cycle of AI systems. It is important that data for AI systems be collected, used, shared, archived and deleted in ways that are consistent with international law and in line with the values and principles set forth in this Recommendation, while respecting relevant national, regional and international legal frameworks. 33. Adequate data protection frameworks and governance mechanisms should be established in a multi stakeholder approach at the national or international level, protected by judicial systems, and ensured throughout the life cycle of AI systems. Data protection frameworks and any related mechanisms should take reference from international data protection principles and standards concerning the collection, use and disclosure of personal data and exercise of their rights by data subjects while ensuring a legitimate aim and a valid legal basis for the processing of personal data, including informed consent. 34. Algorithmic systems require adequate privacy impact assessments, which also include societal and ethical considerations of their use and an innovative use of the privacy by design approach. AI actors need to ensure that they are accountable for the design and implementation of AI systems in such a way as to ensure that personal information is protected throughout the life cycle of the AI system.

Published by The United Nations Educational, Scientific and Cultural Organization (UNESCO) in The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021

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

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