# Fairness-Aware Data Valuation for Supervised Learning

arXiv: https://arxiv.org/abs/2303.16963 · HTML: https://mlsanity.com/paper/2303.16963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2110.14049/) (2021)
- [Why Is My Classifier Discriminatory?](https://mlsanity.com/paper/1805.12002/) (2018)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [Bias in Machine Learning Software: Why? How? What to do?](https://mlsanity.com/paper/2105.12195/) (2021)
- [A Distributional Framework for Data Valuation](https://mlsanity.com/paper/2002.12334/) (2020)
- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [A Benchmark and Comparison of Active Learning for Logistic Regression](https://mlsanity.com/paper/1611.08618/) (2016)
- [Domain Adaptive Transfer Learning with Specialist Models](https://mlsanity.com/paper/1811.07056/) (2018)
- [Data Shapley Valuation for Efficient Batch Active Learning](https://mlsanity.com/paper/2104.08312/) (2021)
- [Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines](https://mlsanity.com/paper/2204.11131/) (2022)
- [Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions](https://mlsanity.com/paper/2207.06273/) (2022)
- [Active learning for imbalanced data under cold start](https://mlsanity.com/paper/2107.07724/) (2021)
- [Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction](https://mlsanity.com/paper/2206.13183/) (2022)

## Built on by (most cited first, 4 of 4)

- [Improving Rule-based Reasoning in LLMs using Neurosymbolic Representations](https://mlsanity.com/paper/2502.01657/) (2025)
- [LossVal: Efficient Data Valuation for Neural Networks](https://mlsanity.com/paper/2412.04158/) (2024)
- [Fair for a few: Improving Fairness in Doubly Imbalanced Datasets](https://mlsanity.com/paper/2506.14306/) (2025)
- [Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories](https://mlsanity.com/paper/2404.19557/) (2024)

## Abstract (arXiv)

Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data valuation framework that can be used to incorporate fairness concerns into a series of ML-related tasks (e.g., data pre-processing, exploratory data analysis, active learning). We propose an entropy-based data valuation metric suited to address our two-pronged goal of maximizing both performance and fairness, which is more computationally efficient than existing metrics. We then show how FADO can be applied as the basis for unfairness mitigation pre-processing techniques. Our methods achieve promising results -- up to a 40 p.p. improvement in fairness at a less than 1 p.p. loss in performance compared to a baseline -- and promote fairness in a data-centric way, where a deeper understanding of data quality takes center stage.