Actively participate in the formulation of international, national, industry, and organizational standards related to artificial intelligence. Enhance the measurability of ethical principles such as security and controllability, transparency and explainability, privacy protection, and diversity and inclusiveness; and simultaneously build corresponding assessment capabilities.

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

Published by Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) in Principles for Accountable Algorithms, Jul 22, 2016 (unconfirmed)

Published by IBM in Principles for Trust and Transparency, May 30, 2018

AI systems should have algorithmic accountability.

Published by Microsoft in Microsoft AI Principles, Jan 17, 2018 (unconfirmed)

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