Article 11: Formulate standards.
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.
Principles for Accountable Algorithms
Published by: Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) in Principles for Accountable Algorithms
Principles for Trust and Transparency
AI systems should have algorithmic accountability.
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.