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

(f) Transparency:

The data used to train AI systems should be transparent.

Principle: Suggested generic principles for the development, implementation and use of AI, Mar 21, 2019

Published by The Extended Working Group on Ethics of Artificial Intelligence (AI) of the World Commission on the Ethics of Scientific Knowledge and Technology (COMEST), UNESCO

Related Principles

(i) Accountability:

Arrangements should be developed that will make possible to attribute accountability for AI driven decisions and the behaviour of AI systems.

Published by The Extended Working Group on Ethics of Artificial Intelligence (AI) of the World Commission on the Ethics of Scientific Knowledge and Technology (COMEST), UNESCO in Suggested generic principles for the development, implementation and use of AI, Mar 21, 2019

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.

Published by Cyberspace Administration of China in Global AI Governance Initiative, October 18, 2023

3. New technology, including AI systems, must be transparent and explainable

For the public to trust AI, it must be transparent. Technology companies must be clear about who trains their AI systems, what data was used in that training and, most importantly, what went into their algorithm’s recommendations. If we are to use AI to help make important decisions, it must be explainable.

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

Transparency

AI systems should be understandable.

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

· Deploy and Monitor:

1 Upon deployment of the AI system, performance metrics relating the AI system’s output, accuracy and alignment to priorities and objectives, as well as its measured impact on individuals and communities should be documented, available and accessible to stakeholders of the AI technology. 2 Information on any system failures, data breaches, system breakdowns, etc. should be logged and stakeholders should be informed about these instances keeping the performance and execution of the AI system transparent. Periodic UI and UX testing should be conducted to avoid the risk of confusion, confirmation of biases, or cognitive fatigue of the AI system.

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