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

• Liberate Data Responsibly

AI is powered by access to data. Machine learning algorithms improve by analyzing more data over time; data access is imperative to achieve more enhanced AI model development and training. Removing barriers to the access of data will help machine learning and deep learning reach their full potential. [Recommendations] • Keep data moving: Governments should eliminate unwarranted data localization mandates and enable secure international data transfers through international agreements and legal tools. • Open public data: While protecting privacy, governments should make useful datasets publicly available when appropriate and provide guidance to startups and small and medium businesses for its reuse. • Support the creation of reliable data sets to test algorithms: Governments should explore non regulatory methods to encourage the development of testing data sets. • Federate access to data: Governments should partner with industry to promote AI tools to access encrypted data for analysis, while not requiring transfer of the data. (Note: Instead of centralizing data from several institutions, federated access to data allows each institution to keep control of their data while enabling joint data analytics across all institutions.)

Principle: AI public policy principles, Oct 18, 2017

Published by Intel

Related Principles

2. Privacy Principles Privacy by Design

o We have implemented an enterprise wide Privacy by Design approach that incorporates privacy and data security into our ML and associated data processing systems. Our ML models seek to minimize access to identifiable information to ensure we are using only the personal data we need to generate insights. ADP is committed to providing individuals with a reasonable opportunity to examine their own personal data and to update it if it is incorrect.

Published by ADP in ADP: Ethics in Artificial Intelligence, 2018 (unconfirmed)

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

Fairness

Use of AI will include safeguards to manage data bias or data quality risks The best use of AI will depend on data quality and relevant data. It will also rely on careful data management to ensure potential data biases are identified and appropriately managed. AI solutions that rely on sub optimal quality data may result in sub optimal project outcomes and recommendations. Algorithms that contain systemic and repeatable errors may lead to prejudiced decisions or outcomes. Projects should clearly demonstrate: a data model that is designed with a focus on diversity and inclusion use of a dataset that is representative for the problem to be solved regular monitoring of data models and outputs.

Published by Government of New South Welsh, Australia in Mandatory Ethical Principles for the use of AI, 2024

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