# Evolved Sample Weights for Bias Mitigation: Effectiveness Depends on the Fairness Objective

arXiv: https://arxiv.org/abs/2511.20909 · HTML: https://mlsanity.com/paper/2511.20909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Fairlearn: Assessing and Improving Fairness of AI Systems](https://mlsanity.com/paper/2303.16626/) (2023)
- [Fairness-Aware Unsupervised Feature Selection](https://mlsanity.com/paper/2106.02216/) (2021)
- [Optimizing fairness tradeoffs in machine learning with multiobjective meta-models](https://mlsanity.com/paper/2304.12190/) (2023)
- [A Suite of Fairness Datasets for Tabular Classification](https://mlsanity.com/paper/2308.00133/) (2023)
- [Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360](https://mlsanity.com/paper/2312.12560/) (2023)
- [Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison](https://mlsanity.com/paper/2008.10296/) (2020)

## Abstract (arXiv)

Machine learning models trained on real-world data may inadvertently make biased predictions that negatively impact marginalized communities. Reweighting, which assigns a weight to each data point used during model training, can mitigate such bias, though sometimes at the cost of predictive accuracy. In this paper, we investigated this trade-off by comparing three methods for generating these weights: (1) evolving them using a Genetic Algorithm (GA), (2) computing them using only dataset characteristics, and (3) assigning equal weights to all data points. Model performance under each strategy was evaluated using paired predictive and fairness metrics. We used two predictive metrics (accuracy and area under the Receiver Operating Characteristic curve) and two fairness metrics (demographic parity and subgroup false negative fairness). By conducting experiments on eleven publicly available datasets (including two medical datasets), we show that evolved sample weights can produce models that achieve better trade-offs between fairness and predictive performance than alternative weighting methods. However, the magnitude of these benefits depends strongly on the choice of fairness objective. Our experiments reveal that the evolved weights were most effective when optimizing for demographic parity-independent of choice of the performance objective-yielding better performance than other weighting strategies on the largest number of datasets.