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

· General requirements

AI should be safe and reliable, and capable of safeguarding against cyberattacks and other unintended consequences

Principle: "ARCC": An Ethical Framework for Artificial Intelligence, Sep 18, 2018

Published by Tencent Research Institute

Related Principles

3. Security and Safety

AI systems should be safe and sufficiently secure against malicious attacks. Safety refers to ensuring the safety of developers, deployers, and users of AI systems by conducting impact or risk assessments and ensuring that known risks have been identified and mitigated. A risk prevention approach should be adopted, and precautions should be put in place so that humans can intervene to prevent harm, or the system can safely disengage itself in the event an AI system makes unsafe decisions autonomous vehicles that cause injury to pedestrians are an illustration of this. Ensuring that AI systems are safe is essential to fostering public trust in AI. Safety of the public and the users of AI systems should be of utmost priority in the decision making process of AI systems and risks should be assessed and mitigated to the best extent possible. Before deploying AI systems, deployers should conduct risk assessments and relevant testing or certification and implement the appropriate level of human intervention to prevent harm when unsafe decisions take place. The risks, limitations, and safeguards of the use of AI should be made known to the user. For example, in AI enabled autonomous vehicles, developers and deployers should put in place mechanisms for the human driver to easily resume manual driving whenever they wish. Security refers to ensuring the cybersecurity of AI systems, which includes mechanisms against malicious attacks specific to AI such as data poisoning, model inversion, the tampering of datasets, byzantine attacks in federated learning5, as well as other attacks designed to reverse engineer personal data used to train the AI. Deployers of AI systems should work with developers to put in place technical security measures like robust authentication mechanisms and encryption. Just like any other software, deployers should also implement safeguards to protect AI systems against cyberattacks, data security attacks, and other digital security risks. These may include ensuring regular software updates to AI systems and proper access management for critical or sensitive systems. Deployers should also develop incident response plans to safeguard AI systems from the above attacks. It is also important for deployers to make a minimum list of security testing (e.g. vulnerability assessment and penetration testing) and other applicable security testing tools. Some other important considerations also include: a. Business continuity plan b. Disaster recovery plan c. Zero day attacks d. IoT devices

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2021

Reliability and safety

Throughout their lifecycle, AI systems should reliably operate in accordance with their intended purpose. This principle aims to ensure that AI systems reliably operate in accordance with their intended purpose throughout their lifecycle. This includes ensuring AI systems are reliable, accurate and reproducible as appropriate. AI systems should not pose unreasonable safety risks, and should adopt safety measures that are proportionate to the magnitude of potential risks. AI systems should be monitored and tested to ensure they continue to meet their intended purpose, and any identified problems should be addressed with ongoing risk management as appropriate. Responsibility should be clearly and appropriately identified, for ensuring that an AI system is robust and safe.

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

Second, the principle of security.

AI shall not harm human beings. AI systems must be secure, applicable and controllable. Personal privacy should be protected and data breach and abuse prevented. AI algorithms must be traceable and transparent and there should be no algorithm discrimination;

Published by Center for International Strategy and Security, Tsinghua University (Tsinghua CISS) in Six AI Principles proposed by Mme Fu Ying, Jan 23, 2019

Safety and security

Safety and security risks should be identified, addressed and mitigated throughout the AI system lifecycle to prevent where possible, and or limit, any potential or actual harm to humans, the environment and ecosystems. Safe and secure AI systems should be enabled through robust frameworks.

Published by United Nations System Chief Executives Board for Coordination in Principles for the Ethical Use of Artificial Intelligence in the United Nations System, Sept 20, 2022

· 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