7. Data Quality Obligation.

Institutions must establish data provenance, and assure quality and relevance for the data input into algorithms. [Explanatory Memorandum] The Data Quality Principle follows from the preceding obligation.
Principle: Universal Guidelines for Artificial Intelligence, Oct 23, 2018

Published by The Public Voice coalition, established by Electronic Privacy Information Center (EPIC)

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

3. Artificial intelligence systems transparency and intelligibility should be improved, with the objective of effective implementation, in particular by:

a. investing in public and private scientific research on explainable artificial intelligence, b. promoting transparency, intelligibility and reachability, for instance through the development of innovative ways of communication, taking into account the different levels of transparency and information required for each relevant audience, c. making organizations’ practices more transparent, notably by promoting algorithmic transparency and the auditability of systems, while ensuring meaningfulness of the information provided, and d. guaranteeing the right to informational self determination, notably by ensuring that individuals are always informed appropriately when they are interacting directly with an artificial intelligence system or when they provide personal data to be processed by such systems, e. providing adequate information on the purpose and effects of artificial intelligence systems in order to verify continuous alignment with expectation of individuals and to enable overall human control on such systems.

Published by 40th International Conference of Data Protection and Privacy Commissioners (ICDPPC) in Declaration On Ethics And Data Protection In Artifical Intelligence, Oct 23, 2018

· 1.3 Robust and Representative Data

To promote the responsible use of data and ensure its integrity at every stage, industry has a responsibility to understand the parameters and characteristics of the data, to demonstrate the recognition of potentially harmful bias, and to test for potential bias before and throughout the deployment of AI systems. AI systems need to leverage large datasets, and the availability of robust and representative data for building and improving AI and machine learning systems is of utmost importance.

Published by Information Technology Industry Council (ITI) in AI Policy Principles, Oct 24, 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

3. Scientific Integrity and Information Quality

The government’s regulatory and non regulatory approaches to AI applications should leverage scientific and technical information and processes. Agencies should hold information, whether produced by the government or acquired by the government from third parties, that is likely to have a clear and substantial influence on important public policy or private sector decisions (including those made by consumers) to a high standard of quality, transparency, and compliance. Consistent with the principles of scientific integrity in the rulemaking and guidance processes, agencies should develop regulatory approaches to AI in a manner that both informs policy decisions and fosters public trust in AI. Best practices include transparently articulating the strengths, weaknesses, intended optimizations or outcomes, bias mitigation, and appropriate uses of the AI application’s results. Agencies should also be mindful that, for AI applications to produce predictable, reliable, and optimized outcomes, data used to train the AI system must be of sufficient quality for the intended use.

Published by The White House Office of Science and Technology Policy (OSTP), United States in Principles for the Stewardship of AI Applications, Jan 13, 2020