Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

requiring developers to make a high confidence safety case prior to deploying models whose capabilities exceed specified thresholds. Post deployment monitoring should also be a key component of assurance for highly capable AI systems as they become more widely adopted. These safety assurances should be subject to independent audits.

Published by IDAIS (International Dialogues on AI Safety) in IDAIS-Venice, Sept 5, 2024

To promote the responsible use of data and ensure its integrity at every stage, industry has a responsibility to understand the parameters and characteristics of the data, to demonstrate the recognition of potentially harmful bias, and to test for potential bias before and throughout the deployment of AI systems. AI systems need to leverage large datasets, and the availability of robust and representative data for building and improving AI and machine learning systems is of utmost importance.

Published by Information Technology Industry Council (ITI) in AI Policy Principles, Oct 24, 2017

Published by The Ministry of Science and ICT (MSIT) and the Korea Information Society Development Institute (KISDI) in National AI Ethical Guidelines, Dec 23, 2020

Published by United Nations Children's Fund (UNICEF) and the Ministry of in Requirements for child-centred AI, Sep 16, 2020