Those responsible for the different phases of the AI system lifecycle should be identifiable and accountable for the outcomes of the AI systems, and human oversight of AI systems should be enabled. This principle aims to acknowledge the relevant organisations' and individuals’ responsibility for the outcomes of the AI systems that they design, develop, deploy and operate. The application of legal principles regarding accountability for AI systems is still developing. Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. This includes both before and after their design, development, deployment and operation. The organisation and individual accountable for the decision should be identifiable as necessary. They must consider the appropriate level of human control or oversight for the particular AI system or use case. AI systems that have a significant impact on an individual's rights should be accountable to external review, this includes providing timely, accurate, and complete information for the purposes of independent oversight bodies.

Published by Department of Industry, Innovation and Science, Australian Government in AI Ethics Principles, Nov 7, 2019

The social implications of computing have grown and will continue to expand as more people have access to implementations of AI. Public policy should work to identify and mitigate discrimination caused by the use of AI and encourage designing in protections against these harms. [Recommendations] • Standing for “Accountable Artificial Intelligence”: Governments, industry and academia should apply the Information Accountability Foundation’s principles to AI. Organizations implementing AI solutions should be able to demonstrate to regulators that they have the right processes, policies and resources in place to meet those principles. • Transparent decisions: Governments should determine which AI implementations require algorithm explainability to mitigate discrimination and harm to individuals.

Published by Intel in AI public policy principles, Oct 18, 2017

1. Accountability for the outcomes of an AI system lies not with the system itself but is apportioned between those who design, develop and deploy it 2. Developers should make efforts to mitigate the risks inherent in the systems they design 3. AI systems should have built in appeals procedures whereby users can challenge significant decisions 4. AI systems should be developed by diverse teams which include experts in the area in which the system will be deployed

Published by Smart Dubai in Dubai's AI Principles, Jan 08, 2019

1. Developers should build systems whose failures can be traced and diagnosed 2. People should be told when significant decisions about them are being made by AI 3. Within the limits of privacy and the preservation of intellectual property, those who deploy AI systems should be transparent about the data and algorithms they use

Published by Smart Dubai in Dubai's AI Principles, Jan 08, 2019

Published by The Treasury Board Secretariat of Canada (TBS) in Seven principles on the use of AI systems in government, Jun 28, 2018 (unconfirmed)