# Penalizing Unfairness in Binary Classification

arXiv: https://arxiv.org/abs/1707.00044 · HTML: https://mlsanity.com/paper/1707.00044/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2017.

## Built on by (most cited first, 25 of 25)

- [Loss-Aversively Fair Classification](https://mlsanity.com/paper/2105.04273/) (2021)
- [D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias](https://mlsanity.com/paper/2208.05126/) (2022)
- [Removing biased data to improve fairness and accuracy](https://mlsanity.com/paper/2102.03054/) (2021)
- [Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation](https://mlsanity.com/paper/2211.12703/) (2022)
- [Survey on Fairness Notions and Related Tensions](https://mlsanity.com/paper/2209.13012/) (2022)
- [Learning Fair Classifiers via Min-Max F-divergence Regularization](https://mlsanity.com/paper/2306.16552/) (2023)
- [Consistent Range Approximation for Fair Predictive Modeling](https://mlsanity.com/paper/2212.10839/) (2022)
- [Intrinsic Fairness-Accuracy Tradeoffs under Equalized Odds](https://mlsanity.com/paper/2405.07393/) (2024)
- [Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach](https://mlsanity.com/paper/2206.03656/) (2022)
- [When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes](https://mlsanity.com/paper/2207.08336/) (2022)
- [Fairness-Driven Private Collaborative Machine Learning](https://mlsanity.com/paper/2109.14376/) (2021)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)
- [Survey on AI Ethics: A Socio-technical Perspective](https://mlsanity.com/paper/2311.17228/) (2023)
- [MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search](https://mlsanity.com/paper/2412.20086/) (2024)
- [SLIDE: a surrogate fairness constraint to ensure fairness consistency](https://mlsanity.com/paper/2202.03165/) (2022)
- [Fair Machine Learning under Limited Demographically Labeled Data](https://mlsanity.com/paper/2106.04757/) (2021)
- [Fair Causal Feature Selection](https://mlsanity.com/paper/2306.10336/) (2023)
- [Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data](https://mlsanity.com/paper/2210.13182/) (2022)
- [Within-group fairness: A guidance for more sound between-group fairness](https://mlsanity.com/paper/2301.08375/) (2023)
- [Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations](https://mlsanity.com/paper/2305.20020/) (2023)
- [Towards Fair and Explainable AI using a Human-Centered AI Approach](https://mlsanity.com/paper/2306.07427/) (2023)
- [Accounting for Model Uncertainty in Algorithmic Discrimination](https://mlsanity.com/paper/2105.04249/) (2021)
- [F3: Fair and Federated Face Attribute Classification with Heterogeneous Data](https://mlsanity.com/paper/2109.02351/) (2021)
- [On Disentangled and Locally Fair Representations](https://mlsanity.com/paper/2205.02673/) (2022)
- [Fair mapping](https://mlsanity.com/paper/2209.00617/) (2022)

## Abstract (arXiv)

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both populations. As a proof of concept, we implement our approach and empirically evaluate its ability to achieve both fairness and accuracy, using datasets from the fields of criminal risk assessment, credit, lending, and college admissions.