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

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.

Principle: Artificial Intelligence Application Criteria, Jul 8, 2019

Published by Megvii

Related Principles

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

(g) Security, safety, bodily and mental integrity

Safety and security of ‘autonomous’ systems materialises in three forms: (1) external safety for their environment and users, (2) reliability and internal robustness, e.g. against hacking, and (3) emotional safety with respect to human machine interaction. All dimensions of safety must be taken into account by AI developers and strictly tested before release in order to ensure that ‘autonomous’ systems do not infringe on the human right to bodily and mental integrity and a safe and secure environment. Special attention should hereby be paid to persons who find themselves in a vulnerable position. Special attention should also be paid to potential dual use and weaponisation of AI, e.g. in cybersecurity, finance, infrastructure and armed conflict.

Published by European Group on Ethics in Science and New Technologies, European Commission in Ethical principles and democratic prerequisites, Mar 9, 2018

Responsible Deployment

Principle: The capacity of an AI agent to act autonomously, and to adapt its behavior over time without human direction, calls for significant safety checks before deployment, and ongoing monitoring. Recommendations: Humans must be in control: Any autonomous system must allow for a human to interrupt an activity or shutdown the system (an “off switch”). There may also be a need to incorporate human checks on new decision making strategies in AI system design, especially where the risk to human life and safety is great. Make safety a priority: Any deployment of an autonomous system should be extensively tested beforehand to ensure the AI agent’s safe interaction with its environment (digital or physical) and that it functions as intended. Autonomous systems should be monitored while in operation, and updated or corrected as needed. Privacy is key: AI systems must be data responsible. They should use only what they need and delete it when it is no longer needed (“data minimization”). They should encrypt data in transit and at rest, and restrict access to authorized persons (“access control”). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices. Think before you act: Careful thought should be given to the instructions and data provided to AI systems. AI systems should not be trained with data that is biased, inaccurate, incomplete or misleading. If they are connected, they must be secured: AI systems that are connected to the Internet should be secured not only for their protection, but also to protect the Internet from malfunctioning or malware infected AI systems that could become the next generation of botnets. High standards of device, system and network security should be applied. Responsible disclosure: Security researchers acting in good faith should be able to responsibly test the security of AI systems without fear of prosecution or other legal action. At the same time, researchers and others who discover security vulnerabilities or other design flaws should responsibly disclose their findings to those who are in the best position to fix the problem.

Published by Internet Society, "Artificial Intelligence and Machine Learning: Policy Paper" in Guiding Principles and Recommendations, Apr 18, 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

· Safety and security

27. Unwanted harms (safety risks), as well as vulnerabilities to attack (security risks) should be avoided and should be addressed, prevented and eliminated throughout the life cycle of AI systems to ensure human, environmental and ecosystem safety and security. Safe and secure AI will be enabled by the development of sustainable, privacy protective data access frameworks that foster better training and validation of AI models utilizing quality data.

Published by The United Nations Educational, Scientific and Cultural Organization (UNESCO) in The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021