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

We should promote the establishment of a testing and assessment system based on AI risk levels, implement agile governance, and carry out tiered and category based management for rapid and effective response. R&D entities should improve the explainability and predictability of AI, increase data authenticity and accuracy, ensure that AI always remains under human control, and build trustworthy AI technologies that can be reviewed, monitored, and traced.

Principle: Global AI Governance Initiative, October 18, 2023

Published by Cyberspace Administration of China

Related Principles

· 1.4. Robustness, security and safety

a) AI systems should be robust, secure and safe throughout their entire lifecycle so that, in conditions of normal use, foreseeable use or misuse, or other adverse conditions, they function appropriately and do not pose unreasonable safety risk. b) To this end, AI actors should ensure traceability, including in relation to datasets, processes and decisions made during the AI system lifecycle, to enable analysis of the AI system’s outcomes and responses to inquiry, appropriate to the context and consistent with the state of art. c) AI actors should, based on their roles, the context, and their ability to act, apply a systematic risk management approach to each phase of the AI system lifecycle on a continuous basis to address risks related to AI systems, including privacy, digital security, safety and bias.

Published by G20 Ministerial Meeting on Trade and Digital Economy in G20 AI Principles, Jun 09, 2019

· 1.4. Robustness, security and safety

a) AI systems should be robust, secure and safe throughout their entire lifecycle so that, in conditions of normal use, foreseeable use or misuse, or other adverse conditions, they function appropriately and do not pose unreasonable safety risk. b) To this end, AI actors should ensure traceability, including in relation to datasets, processes and decisions made during the AI system lifecycle, to enable analysis of the AI system’s outcomes and responses to inquiry, appropriate to the context and consistent with the state of art. c) AI actors should, based on their roles, the context, and their ability to act, apply a systematic risk management approach to each phase of the AI system lifecycle on a continuous basis to address risks related to AI systems, including privacy, digital security, safety and bias.

Published by The Organisation for Economic Co-operation and Development (OECD) in OECD Principles on Artificial Intelligence, May 22, 2019

· Build and Validate:

1 To develop a sound and functional AI system that is both reliable and safe, the AI system’s technical construct should be accompanied by a comprehensive methodology to test the quality of the predictive data based systems and models according to standard policies and protocols. 2 To ensure the technical robustness of an AI system rigorous testing, validation, and re assessment as well as the integration of adequate mechanisms of oversight and controls into its development is required. System integration test sign off should be done with relevant stakeholders to minimize risks and liability. 3 Automated AI systems involving scenarios where decisions are understood to have an impact that is irreversible or difficult to reverse or may involve life and death decisions should trigger human oversight and final determination. Furthermore, AI systems should not be used for social scoring or mass surveillance purposes.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

6. Flexibility

When developing regulatory and non regulatory approaches, agencies should pursue performance based and flexible approaches that can adapt to rapid changes and updates to AI applications. Rigid, design based regulations that attempt to prescribe the technical specifications of AI applications will in most cases be impractical and ineffective, given the anticipated pace with which AI will evolve and the resulting need for agencies to react to new information and evidence. Targeted agency conformity assessment schemes, to protect health and safety, privacy, and other values, will be essential to a successful, and flexible, performance based approach. To advance American innovation, agencies should keep in mind international uses of AI, ensuring that American companies are not disadvantaged by the United States’ regulatory regime.

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

6. Flexibility

When developing regulatory and non regulatory approaches, agencies should pursue performance based and flexible approaches that can adapt to rapid changes and updates to AI applications. Rigid, design based regulations that attempt to prescribe the technical specifications of AI applications will in most cases be impractical and ineffective, given the anticipated pace with which AI will evolve and the resulting need for agencies to react to new information and evidence. Targeted agency conformity assessment schemes, to protect health and safety, privacy, and other values, will be essential to a successful, and flexible, performance based approach. To advance American innovation, agencies should keep in mind international uses of AI, ensuring that American companies are not disadvantaged by the United States’ regulatory regime.

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