# Coping with Mistreatment in Fair Algorithms

arXiv: https://arxiv.org/abs/2102.10750 · HTML: https://mlsanity.com/paper/2102.10750/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [On the relation between accuracy and fairness in binary classification](https://mlsanity.com/paper/1505.05723/) (2015)
- [Fair Hierarchical Clustering](https://mlsanity.com/paper/2006.10221/) (2020)
- [The Final Frontier: Deep Learning in Space](https://mlsanity.com/paper/2001.10362/) (2020)
- [Fair Algorithms for Hierarchical Agglomerative Clustering](https://mlsanity.com/paper/2005.03197/) (2020)
- [Decoupled classifiers for fair and efficient machine learning](https://mlsanity.com/paper/1707.06613/) (2017)
- [Fast Imbalanced Classification of Healthcare Data with Missing Values](https://mlsanity.com/paper/1503.06250/) (2015)

## Abstract (arXiv)

Machine learning actively impacts our everyday life in almost all endeavors and domains such as healthcare, finance, and energy. As our dependence on the machine learning increases, it is inevitable that these algorithms will be used to make decisions that will have a direct impact on the society spanning all resolutions from personal choices to world-wide policies. Hence, it is crucial to ensure that (un)intentional bias does not affect the machine learning algorithms especially when they are required to take decisions that may have unintended consequences. Algorithmic fairness techniques have found traction in the machine learning community and many methods and metrics have been proposed to ensure and evaluate fairness in algorithms and data collection. In this paper, we study the algorithmic fairness in a supervised learning setting and examine the effect of optimizing a classifier for the Equal Opportunity metric. We demonstrate that such a classifier has an increased false positive rate across sensitive groups and propose a conceptually simple method to mitigate this bias. We rigorously analyze the proposed method and evaluate it on several real world datasets demonstrating its efficacy.