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

· Cyberattacks

No AI system should be able to autonomously execute cyberattacks resulting in serious financial losses or equivalent harm.

Principle: IDAIS-Beijing, May 10, 2024

Published by IDAIS (International Dialogues on AI Safety)

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, 2024

5. Governable.

DoD AI systems should be designed and engineered to fulfill their intended function while possessing the ability to detect and avoid unintended harm or disruption, and for human or automated disengagement or deactivation of deployed systems that demonstrate unintended escalatory or other behavior.

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

· ④ Prevention of Harm

AI should not be used for the purpose of inflicting direct or indirect harm on humans. Efforts should be made to develop measures to handle risks and negative consequences associated with AI.

Published by The Ministry of Science and ICT (MSIT) and the Korea Information Society Development Institute (KISDI) in National AI Ethical Guidelines, Dec 23, 2020

Do no harm

AI systems should not be used in ways that cause or exacerbate harm, whether individual or collective, and including harm to social, cultural, economic, natural, and political environments. All stages of an AI system lifecycle should operate in accordance with the purposes, principles and commitments of the Charter of the United Nations. All stages of an AI system lifecycle should be designed, developed, deployed and operated in ways that respect, protect and promote human rights and fundamental freedoms. The intended and unintended impact of AI systems, at any stage in their lifecycle, should be monitored in order to avoid causing or contributing to harm, including violations of human rights and fundamental freedoms.

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