· Right to Privacy, and Data Protection

32. Privacy, a right essential to the protection of human dignity, human autonomy and human agency, must be respected, protected and promoted throughout the life cycle of AI systems. It is important that data for AI systems be collected, used, shared, archived and deleted in ways that are consistent with international law and in line with the values and principles set forth in this Recommendation, while respecting relevant national, regional and international legal frameworks. 33. Adequate data protection frameworks and governance mechanisms should be established in a multi stakeholder approach at the national or international level, protected by judicial systems, and ensured throughout the life cycle of AI systems. Data protection frameworks and any related mechanisms should take reference from international data protection principles and standards concerning the collection, use and disclosure of personal data and exercise of their rights by data subjects while ensuring a legitimate aim and a valid legal basis for the processing of personal data, including informed consent. 34. Algorithmic systems require adequate privacy impact assessments, which also include societal and ethical considerations of their use and an innovative use of the privacy by design approach. AI actors need to ensure that they are accountable for the design and implementation of AI systems in such a way as to ensure that personal information is protected throughout the life cycle of the AI system.
Principle: The Recommendation on the Ethics of Artificial Intelligence, Nov 24, 2021

Published by The United Nations Educational, Scientific and Cultural Organization (UNESCO)

Related Principles

5. Privacy and Data Governance

AI systems should have proper mechanisms in place to ensure data privacy and protection and maintain and protect the quality and integrity of data throughout their entire lifecycle. Data protocols need to be set up to govern who can access data and when data can be accessed. Data privacy and protection should be respected and upheld during the design, development, and deployment of AI systems. The way data is collected, stored, generated, and deleted throughout the AI system lifecycle must comply with applicable data protection laws, data governance legislation, and ethical principles. Some data protection and privacy laws in ASEAN include Malaysia’s Personal Data Protection Act 2010, the Philippines’ Data Privacy Act of 2012, Singapore’s Personal Data Protection Act 2012, Thailand’s Personal Data Protection Act 2019, Indonesia’s Personal Data Protection Law 2022, and Vietnam’s Personal Data Protection Decree 2023. Organisations should be transparent about their data collection practices, including the types of data collected, how it is used, and who has access to it. Organisations should ensure that necessary consent is obtained from individuals before collecting, using, or disclosing personal data for AI development and deployment, or otherwise have appropriate legal basis to collect, use or disclose personal data without consent. Unnecessary or irrelevant data should not be gathered to prevent potential misuse. Data protection and governance frameworks should be set up and adhered to by developers and deployers of AI systems. These frameworks should also be periodically reviewed and updated in accordance with applicable privacy and data protection laws. For example, data protection impact assessments (DPIA) help organisations determine how data processing systems, procedures, or technologies affect individuals’ privacy and eliminate risks that might violate compliance7. However, it is important to note that DPIAs are much narrower in scope than an overall impact assessment for use of AI systems and are not sufficient as an AI risk assessment. Other components will need to be considered for a full assessment of risks associated with AI systems. Developers and deployers of AI systems should also incorporate a privacy by design principle when developing and deploying AI systems. Privacy by design is an approach that embeds privacy in every stage of the system development lifecycle. Data privacy is essential in gaining the public’s trust in technological advances. Another consideration is investing in privacy enhancing technologies to preserve privacy while allowing personal data to be used for innovation. Privacy enhancing technologies include, but are not limited to, differential privacy, where small changes are made to raw data to securely de identify inputs without having a significant impact on the results of the AI system, and zero knowledge proofs (ZKP), where ZKP hide the underlying data and answer simple questions about whether something is true or false without revealing additional information

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

Privacy protection and security

Throughout their lifecycle, AI systems should respect and uphold privacy rights and data protection, and ensure the security of data. This principle aims to ensure respect for privacy and data protection when using AI systems. This includes ensuring proper data governance, and management, for all data used and generated by the AI system throughout its lifecycle. For example, maintaining privacy through appropriate data anonymisation where used by AI systems. Further, the connection between data, and inferences drawn from that data by AI systems, should be sound and assessed in an ongoing manner. This principle also aims to ensure appropriate data and AI system security measures are in place. This includes the identification of potential security vulnerabilities, and assurance of resilience to adversarial attacks. Security measures should account for unintended applications of AI systems, and potential abuse risks, with appropriate mitigation measures.

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

· 2. RESPONSIBILITY MUST BE FULLY ACKNOWLEDGED WHEN CREATING AND USING AI

2.1. Risk based approach. The degree of attention paid to ethical AI issues and the nature of the relevant actions of AI Actors should be proportional to the assessment of the level of risk posed by specific AI technologies and systems for the interests of individuals and society. Risk level assessment shall take into account both known and possible risks, whereby the probability level of threats, as well as their possible scale in the short and long term shall be considered. Making decisions in the field of AI use that significantly affect society and the state should be accompanied by a scientifically verified, interdisciplinary forecast of socio economic consequences and risks and examination of possible changes in the paradigm of value and cultural development of the society. Development and use of an AI systems risk assessment methodology are encouraged in pursuance of this Code. 2.2. Responsible attitude. AI Actors should responsibly treat: • issues related to the influence of AI systems on society and citizens at every stage of the AI systems’ life cycle, i.a. on privacy, ethical, safe and responsible use of personal data; • the nature, degree and extent of damage that may result from the use of AI technologies and systems; • the selection and use of hardware and software utilized in different life cycles of AI systems. At the same time, the responsibility of AI Actors should correspond with the nature, degree and extent of damage that may occur as a result of the use of AI technologies and systems. The role in the life cycle of the AI system, as well as the degree of possible and real influence of a particular AI Actor on causing damage and its extent, should also be taken into account. 2.3. Precautions. When the activities of AI Actors can lead to morally unacceptable consequences for individuals and society, which can be reasonably predicted by the relevant AI Actor, the latter, should take measures to prohibit or limit the occurrence of such consequences. AI Actors shall use the provisions of this Code, including the mechanisms specified in Section 2, to assess the moral unacceptability of such consequences and discuss possible preventive measures. 2.4. No harm. AI Actors should not allow the use of AI technologies for the purpose of causing harm to human life and or health, the property of citizens and legal entities and the environment. Any use, including the design, development, testing, integration or operation of an AI system capable of purposefully causing harm to the environment, human life and or health, the property of citizens and legal entities, is prohibited. 2.5. Identification of AI in communication with a human. AI Actors are encouraged to ensure that users are duly informed of their interactions with AI systems when it affects human rights and critical areas of people’s lives and to ensure that such interaction can be terminated at the request of the user. 2.6. Data security. AI Actors must comply with the national legislation in the field of personal data and secrets protected by law when using AI systems; ensure the security and protection of personal data processed by AI systems or by AI Actors in order to develop and improve the AI systems; develop and integrate innovative methods to counter unauthorized access to personal data by third parties and use high quality and representative datasets obtained without breaking the law from reliable sources. 2.7. Information security. AI Actors should ensure the maximum possible protection from unauthorized interference of third parties in the operation of AI systems; integrate adequate information security technologies, i.a. use internal mechanisms designed to protect the AI system from unauthorized interventions and inform users and developers about such interventions; as well as promote the informing of users about the rules of information security during the use of AI systems. 2.8. Voluntary certification and Code compliance. AI Actors may implement voluntary certification systems to assess the compliance of developed AI technologies with the standards established by the national legislation and this Code. AI Actors may create voluntary certification and labeling systems for AI systems to indicate that these systems have passed voluntary certification procedures and confirm quality standards. 2.9. Control of the recursive self improvement of AI systems. AI Actors are encouraged to cooperate in identifying and verifying information about ways and forms of design of so called universal ("general") AI systems and prevention of possible threats they carry. The issues concerning the use of "general" AI technologies should be under the control of the state.

Published by AI Alliance Russia in AI Ethics Code (revised version), Oct 21, 2022 (unconfirmed)

Principle 2 – Privacy & Security

The privacy and security principle represents overarching values that require AI systems; throughout the AI System Lifecycle; to be built in a safe way that respects the privacy of the data collected as well as upholds the highest levels of data security processes and procedures to keep the data confidential preventing data and system breaches which could lead to reputational, psychological, financial, professional, or other types of harm. AI systems should be designed with mechanisms and controls that provide the possibility to govern and monitor their outcomes and progress throughout their lifecycle to ensure continuous monitoring within the privacy and security principles and protocols set in place.

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

Right to privacy, data protection and data governance

Privacy of individuals and their rights as data subjects must be respected, protected and promoted throughout the lifecycle of AI systems. When considering the use of AI systems, adequate data protection frameworks and data governance mechanisms should be established or enhanced in line with the United Nations Personal Data Protection and Privacy Principles also to ensure the integrity of the data used.

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