# An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings

arXiv: https://arxiv.org/abs/2105.06442 · HTML: https://mlsanity.com/paper/2105.06442/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 70 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Wasserstein Fair Classification](https://mlsanity.com/paper/1907.12059/) (2019)
- [Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making](https://mlsanity.com/paper/1903.10598/) (2019)
- [On the relation between accuracy and fairness in binary classification](https://mlsanity.com/paper/1505.05723/) (2015)
- [Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making](https://mlsanity.com/paper/1806.04959/) (2018)
- [FAE: A Fairness-Aware Ensemble Framework](https://mlsanity.com/paper/2002.00695/) (2020)
- [Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions](https://mlsanity.com/paper/2001.09233/) (2020)
- [Transductive Optimization of Top k Precision](https://mlsanity.com/paper/1510.05976/) (2015)

## Built on by (most cited first, 8 of 8)

- [Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation](https://mlsanity.com/paper/2211.13358/) (2022)
- [Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models](https://mlsanity.com/paper/2206.09875/) (2022)
- [Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions](https://mlsanity.com/paper/2207.06273/) (2022)
- [ABCinML: Anticipatory Bias Correction in Machine Learning Applications](https://mlsanity.com/paper/2206.06960/) (2022)
- [Aequitas Flow: Streamlining Fair ML Experimentation](https://mlsanity.com/paper/2405.05809/) (2024)
- [Systematic Evaluation of Predictive Fairness](https://mlsanity.com/paper/2210.08758/) (2022)
- [Fair-OBNC: Correcting Label Noise for Fairer Datasets](https://mlsanity.com/paper/2410.06214/) (2024)
- [A Conceptual Framework for Using Machine Learning to Support Child Welfare Decisions](https://mlsanity.com/paper/2207.05855/) (2022)

## Abstract (arXiv)

Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure fair outcomes from these systems. The machine learning research community has responded to this challenge with a wide array of proposed fairness-enhancing strategies for ML models, but despite the large number of methods that have been developed, little empirical work exists evaluating these methods in real-world settings. Here, we seek to fill this research gap by investigating the performance of several methods that operate at different points in the ML pipeline across four real-world public policy and social good problems. Across these problems, we find a wide degree of variability and inconsistency in the ability of many of these methods to improve model fairness, but post-processing by choosing group-specific score thresholds consistently removes disparities, with important implications for both the ML research community and practitioners deploying machine learning to inform consequential policy decisions.