· d. Ensuring that AI research and technology is robust, reliable, trustworthy, and operates within secure constraints.

Principle: Tenets, Sep 28, 2016 (unconfirmed)

Published by Partnership on AI

Related Principles

· (7) Innovation

To realize Society 5.0 and continuous innovation in which people evolve along with AI, it is necessary to account for national, industry academia, and public private borders, race, sex, nationality, age, political and religious beliefs, etc. Beyond these boundaries, through a Global perspective we must promote diversification and cooperation between industry academia public private sectors, through the development of human capabilities and technology. To encourage mutual collaboration and partnership between universities, research institutions and private sectors, and the flexible movement of talent. To implement AI efficiently and securely in society, methods for confirming the quality and reliability of AI and for efficient collection and maintenance of data utilized in AI must be promoted. Additionally, the establishment of AI engineering should also be promoted. This engineering includes methods for the development, testing and operation of AI. To ensure the sound development of AI technology, it is necessary to establish an accessible platform in which data from all fields can be mutually utilized across borders with no monopolies, while ensuring privacy and security. In addition, research and development environments should be created in which computer resources and highspeed networks are shared and utilized, to promote international collaboration and accelerate AI research. To promote implementation of AI technology, governments must promote regulatory reform to reduce impeding factors in AI related fields.

Published by Cabinet Office, Government of Japan in Social Principles of Human-centric AI (Draft), Dec 27, 2018

3. Traceable.

DoD’s AI engineering discipline should be sufficiently advanced such that technical experts possess an appropriate understanding of the technology, development processes, and operational methods of its AI systems, including transparent and auditable methodologies, data sources, and design procedure and documentation.

Published by Defense Innovation Board (DIB), Department of Defense (DoD), United States in AI Ethics Principles for DoD, Oct 31, 2019

· 1.3 Robust and Representative Data

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

· 1) Robustness:

Artificial intelligence should be safe and reliable. We are dedicated to accentuating technical robustness and security throughout the research process, providing a secure and reliable system to improve the ability to prevent attack and conduct self repair.

Published by Youth Work Committee of Shanghai Computer Society in Chinese Young Scientists’ Declaration on the Governance and Innovation of Artificial Intelligence, Aug 29, 2019

3. Traceable

The department's AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes and operational methods applicable to AI capabilities, including with transparent and auditable methodologies, data sources and design procedures and documentation.

Published by Department of Defense (DoD), United States in DoD's AI ethical principles, Feb 24, 2020