Unlawful Proxy Discrimination: A Framework for Challenging Inherently Discriminatory Algorithms
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Abstract (arXiv)
Emerging scholarship suggests that the EU legal concept of direct discrimination - where a person is given different treatment on grounds of a protected characteristic - may apply to various algorithmic decision-making contexts. This has important implications: unlike indirect discrimination, there is generally no 'objective justification' stage in the direct discrimination framework, which means that the deployment of directly discriminatory algorithms will usually be unlawful per se. In this paper, we focus on the most likely candidate for direct discrimination in the algorithmic context, termed inherent direct discrimination, where a proxy is inextricably linked to a protected characteristic. We draw on computer science literature to suggest that, in the algorithmic context, 'treatment on the grounds of' needs to be understood in terms of two steps: proxy capacity and proxy use. Only where both elements can be made out can direct discrimination be said to be `on grounds of' a protected characteristic. We analyse the legal conditions of our proposed proxy capacity and proxy use tests. Based on this analysis, we discuss technical approaches and metrics that could be developed or applied to identify inherent direct discrimination in algorithmic decision-making.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 22 Apr 2024.
Builds on
13 of 52 references.
References · 13
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
- 2014Certifying and removing disparate impactcited by 715
- 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
- 2018Path-Specific Counterfactual Fairnesscited by 179
- 2017Fair Inference On Outcomescited by 143
- 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
- 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
- 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
- 2021The Use and Misuse of Counterfactuals in Ethical Machine Learningcited by 52
- 2020What's Sex Got To Do With Fair Machine Learning?cited by 37
- 2022The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairnesscited by 21
- 2023Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Treecited by 14
1 more
- 2022What is Proxy Discrimination?cited by 7
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Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2025No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomycited by 8
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Modelscited by 2
- 2024Debiasing Alternative Data for Credit Underwriting Using Causal Inferencecited by 1
- 2025On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systemscited by 1
- 2026Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disordercited by 0
- 2026Toward Fairness in Machine Learning Models for Predicting Treatment Retention and Premature Discontinuation in Medication for Opioid Use Disorder19 Aug 2026
- 2025On Explaining Proxy Discrimination and Unfairness in Individual Decisions Made by AI Systems30 Sep 2025
- 2025No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy4 Sep 2025
- 2025Engineering the Law-Machine Learning Translation Problem: Developing Legally Aligned Models23 Apr 2025
- 2024Debiasing Alternative Data for Credit Underwriting Using Causal Inference29 Oct 2024