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

· 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.

Principle: Chinese Young Scientists’ Declaration on the Governance and Innovation of Artificial Intelligence, Aug 29, 2019

Published by Youth Work Committee of Shanghai Computer Society

Related Principles

· Article 5: Secure safe and controllable.

Ensure that AI systems operate securely safely, reliably, and controllably throughout their lifecycle. Evaluate system security safety and potential risks, and continuously improve system maturity, robustness, and anti tampering capabilities. Ensure that the system can be supervised and promptly taken over by humans to avoid the negative effects of loss of system control.

Published by Artificial Intelligence Industry Alliance (AIIA), China in Joint Pledge on Artificial Intelligence Industry Self-Discipline (Draft for Comment), May 31, 2019

II. Technical robustness and safety

Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of the AI system, and to adequately cope with erroneous outcomes. AI systems need to be reliable, secure enough to be resilient against both overt attacks and more subtle attempts to manipulate data or algorithms themselves, and they must ensure a fall back plan in case of problems. Their decisions must be accurate, or at least correctly reflect their level of accuracy, and their outcomes should be reproducible. In addition, AI systems should integrate safety and security by design mechanisms to ensure that they are verifiably safe at every step, taking at heart the physical and mental safety of all concerned. This includes the minimisation and where possible the reversibility of unintended consequences or errors in the system’s operation. Processes to clarify and assess potential risks associated with the use of AI systems, across various application areas, should be put in place.

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

3. Artificial intelligence systems transparency and intelligibility should be improved, with the objective of effective implementation, in particular by:

a. investing in public and private scientific research on explainable artificial intelligence, b. promoting transparency, intelligibility and reachability, for instance through the development of innovative ways of communication, taking into account the different levels of transparency and information required for each relevant audience, c. making organizations’ practices more transparent, notably by promoting algorithmic transparency and the auditability of systems, while ensuring meaningfulness of the information provided, and d. guaranteeing the right to informational self determination, notably by ensuring that individuals are always informed appropriately when they are interacting directly with an artificial intelligence system or when they provide personal data to be processed by such systems, e. providing adequate information on the purpose and effects of artificial intelligence systems in order to verify continuous alignment with expectation of individuals and to enable overall human control on such systems.

Published by 40th International Conference of Data Protection and Privacy Commissioners (ICDPPC) in Declaration On Ethics And Data Protection In Artifical Intelligence, Oct 23, 2018

3. Technical reliability, Safety and security

Artificial intelligence solutions should be able to make accurate and effective decisions, while providing adequate security and defense against external attacks. Artificial intelligence solutions should be extensively tested, used with care and monitored.

Published by Megvii in Artificial Intelligence Application Criteria, Jul 8, 2019

Fifth principle: Reliability

AI enabled systems must be demonstrably reliable, robust and secure. The MOD’s AI enabled systems must be suitably reliable; they must fulfil their intended design and deployment criteria and perform as expected, within acceptable performance parameters. Those parameters must be regularly reviewed and tested for reliability to be assured on an ongoing basis, particularly as AI enabled systems learn and evolve over time, or are deployed in new contexts. Given Defence’s unique operational context and the challenges of the information environment, this principle also requires AI enabled systems to be secure, and a robust approach to cybersecurity, data protection and privacy. MOD personnel working with or alongside AI enabled systems can build trust in those systems by ensuring that they have a suitable level of understanding of the performance and parameters of those systems, as articulated in the principle of understanding.

Published by The Ministry of Defence (MOD), United Kingdom in Ethical Principles for AI in Defence, Jun 15, 2022