# Fairness Constraints: Mechanisms for Fair Classification

arXiv: https://arxiv.org/abs/1507.05259 · HTML: https://mlsanity.com/paper/1507.05259/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2015.

## Built on by (most cited first, 10 of 10)

- [Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search](https://mlsanity.com/paper/2109.05433/) (2021)
- [Mitigating Test-Time Bias for Fair Image Retrieval](https://mlsanity.com/paper/2305.19329/) (2023)
- [Counterfactual Fairness Is Basically Demographic Parity](https://mlsanity.com/paper/2208.03843/) (2022)
- [On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model](https://mlsanity.com/paper/2310.05598/) (2023)
- [A Comprehensive Guide to Explainable AI: From Classical Models to LLMs](https://mlsanity.com/paper/2412.00800/) (2024)
- [COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation](https://mlsanity.com/paper/2210.15500/) (2022)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)
- [Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization](https://mlsanity.com/paper/2111.03638/) (2021)
- [Fair Bayesian Data Selection via Generalized Discrepancy Measures](https://mlsanity.com/paper/2511.07032/) (2025)
- [Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks](https://mlsanity.com/paper/2202.05395/) (2022)

## Abstract (arXiv)

Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these automated decisions can lead, even in the absence of intent, to a lack of fairness, i.e., their outcomes can disproportionately hurt (or, benefit) particular groups of people sharing one or more sensitive attributes (e.g., race, sex). In this paper, we introduce a flexible mechanism to design fair classifiers by leveraging a novel intuitive measure of decision boundary (un)fairness. We instantiate this mechanism with two well-known classifiers, logistic regression and support vector machines, and show on real-world data that our mechanism allows for a fine-grained control on the degree of fairness, often at a small cost in terms of accuracy.