# A statistical approach to detect sensitive features in a group fairness setting

arXiv: https://arxiv.org/abs/2305.06994 · HTML: https://mlsanity.com/paper/2305.06994/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 21 of 21)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [FairBatch: Batch Selection for Model Fairness](https://mlsanity.com/paper/2012.01696/) (2020)
- [A Confidence-Based Approach for Balancing Fairness and Accuracy](https://mlsanity.com/paper/1601.05764/) (2016)
- [A Review of Feature Selection Methods Based on Mutual Information](https://mlsanity.com/paper/1509.07577/) (2015)
- [AdaFair: Cumulative Fairness Adaptive Boosting](https://mlsanity.com/paper/1909.08982/) (2019)
- [Fair Kernel Learning](https://mlsanity.com/paper/1710.05578/) (2017)
- [Robust Learning with the Hilbert-Schmidt Independence Criterion](https://mlsanity.com/paper/1910.00270/) (2019)
- [FAE: A Fairness-Aware Ensemble Framework](https://mlsanity.com/paper/2002.00695/) (2020)
- [Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness](https://mlsanity.com/paper/1911.04322/) (2019)
- [LimeOut: An Ensemble Approach To Improve Process Fairness](https://mlsanity.com/paper/2006.10531/) (2020)
- [Reducing Unintended Bias of ML Models on Tabular and Textual Data](https://mlsanity.com/paper/2108.02662/) (2021)
- [A novel approach for Fair Principal Component Analysis based on eigendecomposition](https://mlsanity.com/paper/2208.11362/) (2022)
- [Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization](https://mlsanity.com/paper/2006.06137/) (2020)

## Built on by (most cited first, 2 of 2)

- [Fairness-Aware Graph Representation Learning with Limited Demographic Information](https://mlsanity.com/paper/2511.13540/) (2025)
- [Responsible Benchmarking of Fairness for Automatic Speech Recognition](https://mlsanity.com/paper/2605.10615/) (2026)

## 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.