5. User Data Rights:

AI must be designed to protect user data and preserve the user’s power over access and uses.
Principle: Everyday Ethics for Artificial Intelligence: Five Areas of Ethical Focus, Sep 6, 2018

Published by IBM

Related Principles

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

12) Personal Privacy

People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilize that data.

Published by Future of Life Institute (FLI), Beneficial AI 2017 in Asilomar AI Principles, Jan 3-8, 2017

• 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.)

Published by Intel in AI public policy principles, Oct 18, 2017

6. Data Security and Privacy Protection

In the development and use of artificial intelligence solutions, users' personal privacy and data security need to be strictly protected.

Published by Megvii in Artificial Intelligence Application Criteria, Jul 8, 2019

4. Privacy Protection

The development of artificial intelligence needs to ensure the user's data security, artificial intelligence development must not be at the expense of user privacy, need to strengthen data protection legislation, enrich the technical route of artificial intelligence, and constantly strengthen user privacy protection in artificial intelligence applications.

Published by Shanghai Advisory Committee of Experts on Artificial Intelligence Industry Security in Shanghai Initiative for the Safe Development of Artificial Intelligence, Aug 30, 2019