6. Unlawful biases or discriminations that may result from the use of data in artificial intelligence should be reduced and mitigated, including by:

a. ensuring the respect of international legal instruments on human rights and non discrimination, b. investing in research into technical ways to identify, address and mitigate biases, c. taking reasonable steps to ensure the personal data and information used in automated decision making is accurate, up to date and as complete as possible, and d. elaborating specific guidance and principles in addressing biases and discrimination, and promoting individuals’ and stakeholders’ awareness.
Principle: Declaration On Ethics And Data Protection In Artifical Intelligence, Oct 23, 2018

Published by 40th International Conference of Data Protection and Privacy Commissioners (ICDPPC)

Related Principles

V. Diversity, non discrimination and fairness

Data sets used by AI systems (both for training and operation) may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models. The continuation of such biases could lead to (in)direct discrimination. Harm can also result from the intentional exploitation of (consumer) biases or by engaging in unfair competition. Moreover, the way in which AI systems are developed (e.g. the way in which the programming code of an algorithm is written) may also suffer from bias. Such concerns should be tackled from the beginning of the system’ development. Establishing diverse design teams and setting up mechanisms ensuring participation, in particular of citizens, in AI development can also help to address these concerns. It is advisable to consult stakeholders who may directly or indirectly be affected by the system throughout its life cycle. AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility through a universal design approach to strive to achieve equal access for persons with disabilities.

Published by European Commission in Key requirements for trustworthy AI, Apr 8, 2019

5. Non Discrimination

Discrimination concerns the variability of AI results between individuals or groups of people based on the exploitation of differences in their characteristics that can be considered either intentionally or unintentionally (such as ethnicity, gender, sexual orientation or age), which may negatively impact such individuals or groups. Direct or indirect discrimination through the use of AI can serve to exploit prejudice and marginalise certain groups. Those in control of algorithms may intentionally try to achieve unfair, discriminatory, or biased outcomes in order to exclude certain groups of persons. Intentional harm can, for instance, be achieved by explicit manipulation of the data to exclude certain groups. Harm may also result from exploitation of consumer biases or unfair competition, such as homogenisation of prices by means of collusion or non transparent market. Discrimination in an AI context can occur unintentionally due to, for example, problems with data such as bias, incompleteness and bad governance models. Machine learning algorithms identify patterns or regularities in data, and will therefore also follow the patterns resulting from biased and or incomplete data sets. An incomplete data set may not reflect the target group it is intended to represent. While it might be possible to remove clearly identifiable and unwanted bias when collecting data, data always carries some kind of bias. Therefore, the upstream identification of possible bias, which later can be rectified, is important to build in to the development of AI. Moreover, it is important to acknowledge that AI technology can be employed to identify this inherent bias, and hence to support awareness training on our own inherent bias. Accordingly, it can also assist us in making less biased decisions.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

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

5. Empowerment of every individual should be promoted, and the exercise of individuals’ rights should be encouraged, as well as the creation of opportunities for public engagement, in particular by:

a. respecting data protection and privacy rights, including where applicable the right to information, the right to access, the right to object to processing and the right to erasure, and promoting those rights through education and awareness campaigns, b. respecting related rights including freedom of expression and information, as well as non discrimination, c. recognizing that the right to object or appeal applies to technologies that influence personal development or opinions and guaranteeing, where applicable, individuals’ right not to be subject to a decision based solely on automated processing if it significantly affects them and, where not applicable, guaranteeing individuals’ right to challenge such decision, d. using the capabilities of artificial intelligence systems to foster an equal empowerment and enhance public engagement, for example through adaptable interfaces and accessible tools.

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

7. Fairness and Non Discrimination

Agencies should consider in a transparent manner the impacts that AI applications may have on discrimination. AI applications have the potential of reducing present day discrimination caused by human subjectivity. At the same time, applications can, in some instances, introduce real world bias that produces discriminatory outcomes or decisions that undermine public trust and confidence in AI. When considering regulations or non regulatory approaches related to AI applications, agencies should consider, in accordance with law, issues of fairness and non discrimination with respect to outcomes and decisions produced by the AI application at issue, as well as whether the AI application at issue may reduce levels of unlawful, unfair, or otherwise unintended discrimination as compared to existing processes.

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