# A Principled Approach for Data Bias Mitigation

arXiv: https://arxiv.org/abs/2405.12312 · HTML: https://mlsanity.com/paper/2405.12312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 3 of 3)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Dataset search: a survey](https://mlsanity.com/paper/1901.00735/) (2019)

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

- [Data Bias Mitigation under Coverage Constraints & The Price of Fairness](https://mlsanity.com/paper/2606.20461/) (2026)

## Abstract (arXiv)

The widespread use of machine learning and data-driven algorithms for decision making has been steadily increasing over many years. Bias in the data can adversely affect this decision-making. We present a new mitigation strategy to address data bias. Our methods are explainable and come with mathematical guarantees of correctness. They can take advantage of new work on table discovery to find new tuples that can be added to a dataset to create real datasets that are unbiased or less biased. Our framework covers data with non-binary labels and with multiple sensitive attributes. Hence, we are able to measure and mitigate bias that does not appear over a single attribute (or feature), but only intersectionally, when considering a combination of attributes. We evaluate our techniques on publicly available datasets and provide a theoretical analysis of our results, highlighting novel insights into data bias.