# Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360

arXiv: https://arxiv.org/abs/2312.12560 · HTML: https://mlsanity.com/paper/2312.12560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [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)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients](https://mlsanity.com/paper/2009.13447/) (2020)
- [D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias](https://mlsanity.com/paper/2208.05126/) (2022)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)

## Built on by (most cited first, 3 of 3)

- [Data Augmentation via Diffusion Model to Enhance AI Fairness](https://mlsanity.com/paper/2410.15470/) (2024)
- [On Optimal Steering to Achieve Exact Fairness](https://mlsanity.com/paper/2509.15759/) (2025)
- [Evolved Sample Weights for Bias Mitigation: Effectiveness Depends on the Fairness Objective](https://mlsanity.com/paper/2511.20909/) (2025)

## Abstract (arXiv)

Fairness AI aims to detect and alleviate bias across the entire AI development life cycle, encompassing data curation, modeling, evaluation, and deployment-a pivotal aspect of ethical AI implementation. Addressing data bias, particularly concerning sensitive attributes like gender and race, reweighting samples proves efficient for fairness AI. This paper contributes a systematic examination of reweighting samples for traditional machine learning (ML) models, employing five models for binary classification on the Adult Income and COMPUS datasets with various protected attributes. The study evaluates prediction results using five fairness metrics, uncovering the nuanced and model-specific nature of reweighting sample effectiveness in achieving fairness in traditional ML models, as well as revealing the complexity of bias dynamics.