· Explain accordingly

AI models, products, applications, and services should, on the basis of continuous efforts to improve their own transparency and interpretability, further consider the cognitive levels, self needs, and expression abilities of children at different stages, provide the corresponding level of transparency and explanations, and provide children with effective feedback mechanisms and interaction methods.
Principle: Artificial Intelligence for Children: Beijing Principles, Sep 14, 2020

Published by Beijing Academy of Artificial Intelligence (BAAI), Peking University, Tsinghua University and the Chinese Academy of Sciences, together with enterprises that focus on AI development.

Related Principles

· 6. Respect for (& Enhancement of) Human Autonomy

AI systems should be designed not only to uphold rights, values and principles, but also to protect citizens in all their diversity from governmental and private abuses made possible by AI technology, ensuring a fair distribution of the benefits created by AI technologies, protect and enhance a plurality of human values, and enhance self determination and autonomy of individual users and communities. AI products and services, possibly through "extreme" personalisation approaches, may steer individual choice by potentially manipulative "nudging". At the same time, people are increasingly willing and expected to delegate decisions and actions to machines (e.g. recommender systems, search engines, navigation systems, virtual coaches and personal assistants). Systems that are tasked to help the user, must provide explicit support to the user to promote her his own preferences, and set the limits for system intervention, ensuring that the overall wellbeing of the user as explicitly defined by the user her himself is central to system functionality.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

3. Artificial intelligence systems transparency and intelligibility should be improved, with the objective of effective implementation, in particular by:

a. investing in public and private scientific research on explainable artificial intelligence, b. promoting transparency, intelligibility and reachability, for instance through the development of innovative ways of communication, taking into account the different levels of transparency and information required for each relevant audience, c. making organizations’ practices more transparent, notably by promoting algorithmic transparency and the auditability of systems, while ensuring meaningfulness of the information provided, and d. guaranteeing the right to informational self determination, notably by ensuring that individuals are always informed appropriately when they are interacting directly with an artificial intelligence system or when they provide personal data to be processed by such systems, e. providing adequate information on the purpose and effects of artificial intelligence systems in order to verify continuous alignment with expectation of individuals and to enable overall human control on such systems.

Published by 40th International Conference of Data Protection and Privacy Commissioners (ICDPPC) in Declaration On Ethics And Data Protection In Artifical Intelligence, Oct 23, 2018

Transparent and Accountable.

We will provide appropriate transparency to the public and our customers regarding our AI methods, applications, and uses within the bounds of security, technology, and releasability by law and policy, and consistent with the Principles of Intelligence Transparency for the IC. We will develop and employ mechanisms to identify responsibilities and provide accountability for the use of AI and its outcomes.

Published by Intelligence Community (IC), United States in Principles of Artificial Intelligence Ethics for the Intelligence Community, Jul 23, 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

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