# Measuring and Mitigating Bias for Tabular Datasets with Multiple Protected Attributes

arXiv: https://arxiv.org/abs/2405.19300 · HTML: https://mlsanity.com/paper/2405.19300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 11 of 11)

- [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)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [De-biasing "bias" measurement](https://mlsanity.com/paper/2205.05770/) (2022)
- [Fairness in Supervised Learning: An Information Theoretic Approach](https://mlsanity.com/paper/1801.04378/) (2018)
- [InfoFair: Information-Theoretic Intersectional Fairness](https://mlsanity.com/paper/2105.11069/) (2021)
- [Fairness with Overlapping Groups](https://mlsanity.com/paper/2006.13485/) (2020)

## Built on by (most cited first, 1 of 1)

- [(Un)certainty of (Un)fairness: Preference-Based Selection of Certainly Fair Decision-Makers](https://mlsanity.com/paper/2409.12677/) (2024)

## Abstract (arXiv)

Motivated by the recital (67) of the current corrigendum of the AI Act in the European Union, we propose and present measures and mitigation strategies for discrimination in tabular datasets. We specifically focus on datasets that contain multiple protected attributes, such as nationality, age, and sex. This makes measuring and mitigating bias more challenging, as many existing methods are designed for a single protected attribute. This paper comes with a twofold contribution: Firstly, new discrimination measures are introduced. These measures are categorized in our framework along with existing ones, guiding researchers and practitioners in choosing the right measure to assess the fairness of the underlying dataset. Secondly, a novel application of an existing bias mitigation method, FairDo, is presented. We show that this strategy can mitigate any type of discrimination, including intersectional discrimination, by transforming the dataset. By conducting experiments on real-world datasets (Adult, Bank, COMPAS), we demonstrate that de-biasing datasets with multiple protected attributes is possible. All transformed datasets show a reduction in discrimination, on average by 28%. Further, these datasets do not compromise any of the tested machine learning models' performances significantly compared to the original datasets. Conclusively, this study demonstrates the effectiveness of the mitigation strategy used and contributes to the ongoing discussion on the implementation of the European Union's AI Act.