The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Reconsidering discrimination grounds in the data economy: An EU comparison of national constitutions
Open accessAbstract
The principle of equal treatment (i.e., all people have the right to be treated equally) is protected by non-discrimination provisions in national constitutions across the EU as well as the EU Charter of Fundamental Rights (CFEU). These provisions specify which grounds (e.g., gender, race, religion) are prohibited to use as the basis for making decisions on people, such as offering a person a job. In the data economy, in which large amounts of personal data are collected and analyzed, it has become possible to make decisions on people on the basis of all kinds of grounds, also grounds that are not protected in anti-discrimination law (e.g., zip code, shoe size, wealth). Even though mostly unintentional, patterns revealed by sophisticated data analysis can turn out to be discriminatory, either directly or indirectly. Particularly indirect discrimination (i.e., discrimination by proxy) can be hard to discover and enforce. From a substantive perspective, these technological developments also raise the question which discrimination grounds should be protected, since discrimination grounds are in flux and not harmonized across the EU. In this paper, through legal comparison, discrimination grounds across EU national constitutions and the CFEU are compared, to identify overlaps and differences. This overview is then used to start the discussion on the extent to which current legislation is still appropriate in the data economy or should perhaps be reconsidered.
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The level of protection against discrimination varies across EU member states in terms of the number of grounds listed and the wording of the enumeration.
“It is important to note that throughout European constitutions, there exists a clear lack of harmonisation of discrimination grounds and whether an exhaustive or non-exhaustive enumeration is used (Custers, 2023).”
From Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets (2024) · cites this paper as Custers, 2023
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Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI
Wachter, S., Mittelstadt, B., & Russell, C. (2021) · Computer Law & Security Review · doi:10.1016/j.clsr.2021.105567 · Cited by 336
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Heidary, K., van der Rest, J.-P., & Custers, B. (2024) · Internet Policy Review · doi:10.14763/2024.4.1809 · Cited by 11
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Metadata
- Authors
- Custers, B.
- Affiliations
- Leiden University (NL)
- Corresponding author
- Custers, Bart
- ORCID
- Custers, Bart
- Venue
- Computer Law & Security Review
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 50
- Pages
- 105851
- ISSN
- 2212-473X (ISSN-L)
- OpenAlex
- W4386003923
- Cited by
- 5
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3
- FWCI
- 8.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computer law & security review (publishedVersion, cc-by)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- Topics
- Social Sciences › Law › Discrimination and Equality Law
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- data economy, anti-discrimination law, EU Charter of Fundamental Rights, national constitutions, indirect discrimination, algorithmic discrimination, personal data, EU harmonization, equal treatment, legal comparison
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