According to Recital 71 of the GDPR, “the controller should use appropriate mathematical or statistical procedures for the profiling, implement technical and organizational measures appropriate to ensure, in particular, that factors which result in inaccuracies in personal data are corrected and the risk of errors is minimized, secure personal data in a manner that takes account of the potential risks involved for the interests and rights of the data subject and that prevents, inter alia, discriminatory effects on natural persons on the basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health status or sexual orientation, or that result in measures having such an effect.”
This paragraph is strictly linked to Article 21 of the EU Charter of Fundamental Rights, which states that “[a]ny discrimination based on any ground such as sex, race, color, ethnic or social origin, genetic features, language, religion or belief, political or any other opinion, membership of a national minority, property, birth, disability, age or sexual orientation shall be prohibited.” Meanwhile, the EDPB provides a definition of fairness in its Guidelines on Data Protection by Design and by Default, which states that “[f]airness is an overarching principle which requires that personal data shall not be processed in a way that is detrimental, discriminatory, unexpected or misleading to the data subject.”
Discrimination is, therefore, a dramatic violation of the fairness principle. However, in the AI field, biases constitute a formidable threat against this principle, because they could lead to potential stigmatization or discrimination of isolated individuals or entire communities.
1EDPB (2019) Guidelines 4/2019 on Article 25 Data Protection by Design and by Default (version for public consultation). European Data Protection Board, Brussels. Available at: https://edpb.europa.eu/our-work-tools/public-consultations-art-704/2019/guidelines-42019-article-25-data-protection-design_es (accessed 20 May 2020). ↑
2Mittelstadt, B. and L. Floridi, L. (2016) ‘The ethics of big data: current and foreseeable issues in biomedical context’, Science and Engineering Ethics 22(2): 303-341. ↑