A statistical approach to detect sensitive features in a group fairness setting
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Abstract (arXiv)
The use of machine learning models in decision support systems with high societal impact raised concerns about unfair (disparate) results for different groups of people. When evaluating such unfair decisions, one generally relies on predefined groups that are determined by a set of features that are considered sensitive. However, such an approach is subjective and does not guarantee that these features are the only ones to be considered as sensitive nor that they entail unfair (disparate) outcomes. In this paper, we propose a preprocessing step to address the task of automatically recognizing sensitive features that does not require a trained model to verify unfair results. Our proposal is based on the Hilber-Schmidt independence criterion, which measures the statistical dependence of variable distributions. We hypothesize that if the dependence between the label vector and a candidate is high for a sensitive feature, then the information provided by this feature will entail disparate performance measures between groups. Our empirical results attest our hypothesis and show that several features considered as sensitive in the literature do not necessarily entail disparate (unfair) results.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 45 references that are in the map. First posted on arXiv 11 May 2023.
Builds on
21 of 45 references.
References · 21
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9 more
- 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
- 2017Fair Kernel Learningcited by 39
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- 2020FAE: A Fairness-Aware Ensemble Frameworkcited by 34
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.