· "Black box" technology

Promote algorithmic transparency and algorithmic audit, to achieve understandable and explainable AI systems
Principle: "ARCC": An Ethical Framework for Artificial Intelligence, Sep 18, 2018

Published by Tencent Research Institute

Related Principles

· Article 6: Transparent and explainable.

Continuously improve the transparency of artificial intelligence systems. Regarding system decision making processes, data structures, and the intent of system developers and technological implementers: be capable of accurate description, monitoring, and reproduction; and realize explainability, predictability, traceability, and verifiability for algorithmic logic, system decisions, and action outcomes.

Published by Artificial Intelligence Industry Alliance (AIIA), China in Joint Pledge on Artificial Intelligence Industry Self-Discipline (Draft for Comment), May 31, 2019

· Article 9: Diversity and inclusivity.

Promote the inclusiveness, diversity, and universality of artificial intelligence systems. Strengthen cross domain, interdisciplinary, and cross border cooperation and exchange, and solidify an artificial intelligence governance consensus. Strive to achieve diversification of R&D personnel and comprehensive training data for artificial intelligence systems. Continually test and validate algorithms, so that they do not discriminate against users based on race, gender, nationality, age, religious beliefs, etc.

Published by Artificial Intelligence Industry Alliance (AIIA), China in Joint Pledge on Artificial Intelligence Industry Self-Discipline (Draft for Comment), May 31, 2019

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

5. Fairness

a. Ensure that algorithmic decisions do not create discriminatory or unjust impacts across different demographic lines (e.g. race, sex, etc.). b. To develop and include monitoring and accounting mechanisms to avoid unintentional discrimination when implementing decision making systems. c. To consult a diversity of voices and demographics when developing systems, applications and algorithms.

Published by Personal Data Protection Commission (PDPC), Singapore in A compilation of existing AI ethical principles (Annex A), Jan 21, 2020

· Algorithmic fairness

Ethics by design (EBD): ensure that algorithm is reasonable, and date is accurate, up to date, complete, relevant, unbiased and representative, and take technical measures to identify, solve and eliminate bias Formulate guidelines and principles on solving bias and discrimination, potential mechanisms include algorithmic transparency, quality review, impact assessment, algorithmic audit, supervision and review, ethical board, etc.

Published by Tencent Research Institute in "ARCC": An Ethical Framework for Artificial Intelligence, Sep 18, 2018