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

Public Safety Obligation

The Public Safety Obligation recognizes that AI systems control devices in the physical world. For this reason, institutions must both assess risks and take precautionary measures as appropriate.

Principle: Universal Guidelines for AI, Oct, 2018

Published by Center for AI and Digital Policy

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

Cybersecurity Obligation

The Cybersecurity Obligation follows from the Public Safety Obligation and underscores the risk that even well designed systems may be the target of hostile actors. Those who develop and deploy AI systems must take these risks into account.

Published by Center for AI and Digital Policy in Universal Guidelines for AI, Oct, 2018

Principle 7 – Accountability & Responsibility

The accountability and responsibility principle holds designers, vendors, procurers, developers, owners and assessors of AI systems and the technology itself ethically responsible and liable for the decisions and actions that may result in potential risk and negative effects on individuals and communities. Human oversight, governance, and proper management should be demonstrated across the entire AI System Lifecycle to ensure that proper mechanisms are in place to avoid harm and misuse of this technology. AI systems should never lead to people being deceived or unjustifiably impaired in their freedom of choice. The designers, developers, and people who implement the AI system should be identifiable and assume responsibility and accountability for any potential damage the technology has on individuals or communities, even if the adverse impact is unintended. The liable parties should take necessary preventive actions as well as set risk assessment and mitigation strategy to minimize the harm due to the AI system. The accountability and responsibility principle is closely related to the fairness principle. The parties responsible for the AI system should ensure that the fairness of the system is maintained and sustained through control mechanisms. All parties involved in the AI System Lifecycle should consider and action these values in their decisions and execution.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

8. Public Safety Obligation.

Institutions must assess the public safety risks that arise from the deployment of AI systems that direct or control physical devices, and implement safety controls. [Explanatory Memorandum] The Public Safety Obligation recognizes that AI systems control devices in the physical world. For this reason, institutions must both assess risks and take precautionary measures as appropriate.

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC) in Universal Guidelines for Artificial Intelligence, Oct 23, 2018

9. Cybersecurity Obligation.

Institutions must secure AI systems against cybersecurity threats. [Explanatory Memorandum] The Cybersecurity Obligation follows from the Public Safety Obligation and underscores the risk that even well designed systems may be the target of hostile actors. Those who develop and deploy AI systems must take these risks into account.

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC) in Universal Guidelines for Artificial Intelligence, Oct 23, 2018