# A statistical framework for fair predictive algorithms

arXiv: https://arxiv.org/abs/1610.08077 · HTML: https://mlsanity.com/paper/1610.08077/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2016.

## Built on by (most cited first, 30 of 38)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?](https://mlsanity.com/paper/2403.04547/) (2024)
- [Fairness in Ranking under Uncertainty](https://mlsanity.com/paper/2107.06720/) (2021)
- [An Information-theoretic Approach to Distribution Shifts](https://mlsanity.com/paper/2106.03783/) (2021)
- [Bayes-Optimal Classifiers under Group Fairness](https://mlsanity.com/paper/2202.09724/) (2022)
- [Prompting Fairness: Integrating Causality to Debias Large Language Models](https://mlsanity.com/paper/2403.08743/) (2024)
- [A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models](https://mlsanity.com/paper/2106.12887/) (2021)
- [FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations](https://mlsanity.com/paper/2304.14252/) (2023)
- [Removing biased data to improve fairness and accuracy](https://mlsanity.com/paper/2102.03054/) (2021)
- [Fair Bayes-Optimal Classifiers Under Predictive Parity](https://mlsanity.com/paper/2205.07182/) (2022)
- [A Reduction to Binary Approach for Debiasing Multiclass Datasets](https://mlsanity.com/paper/2205.15860/) (2022)
- [Bias-inducing geometries: an exactly solvable data model with fairness implications](https://mlsanity.com/paper/2205.15935/) (2022)
- [Group Meritocratic Fairness in Linear Contextual Bandits](https://mlsanity.com/paper/2206.03150/) (2022)
- [Costs and Benefits of Fair Regression](https://mlsanity.com/paper/2106.08812/) (2021)
- [Improving LLM Group Fairness on Tabular Data via In-Context Learning](https://mlsanity.com/paper/2412.04642/) (2024)
- [FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee](https://mlsanity.com/paper/2211.15072/) (2022)
- [Regression under demographic parity constraints via unlabeled post-processing](https://mlsanity.com/paper/2407.15453/) (2024)
- [More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias](https://mlsanity.com/paper/2207.07723/) (2022)
- [AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias](https://mlsanity.com/paper/2407.19655/) (2024)
- [Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care](https://mlsanity.com/paper/2503.16456/) (2025)
- [Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers](https://mlsanity.com/paper/2202.04504/) (2022)
- [Data augmentation and explainability for bias discovery and mitigation in deep learning](https://mlsanity.com/paper/2308.09464/) (2023)
- [A survey on bias in machine learning research](https://mlsanity.com/paper/2308.11254/) (2023)
- [Achievable Fairness on Your Data With Utility Guarantees](https://mlsanity.com/paper/2402.17106/) (2024)
- [FairRR: Pre-Processing for Group Fairness through Randomized Response](https://mlsanity.com/paper/2403.07780/) (2024)
- [Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)](https://mlsanity.com/paper/2106.08086/) (2021)
- [Bias-Tolerant Fair Classification](https://mlsanity.com/paper/2107.03207/) (2021)
- [Fair Decision-Making for Food Inspections](https://mlsanity.com/paper/2108.05523/) (2021)
- [Uncovering the Source of Machine Bias](https://mlsanity.com/paper/2201.03092/) (2022)
- [De-biased Representation Learning for Fairness with Unreliable Labels](https://mlsanity.com/paper/2208.00651/) (2022)

## Abstract (arXiv)

Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers. It is argued that because computer models do not hold personal prejudice, the predictions they produce will be equally free from prejudice. There is growing recognition that employing algorithms does not remove the potential for bias, and can even amplify it, since training data were inevitably generated by a process that is itself biased. In this paper, we provide a probabilistic definition of algorithmic bias. We propose a method to remove bias from predictive models by removing all information regarding protected variables from the permitted training data. Unlike previous work in this area, our framework is general enough to accommodate arbitrary data types, e.g. binary, continuous, etc. Motivated by models currently in use in the criminal justice system that inform decisions on pre-trial release and paroling, we apply our proposed method to a dataset on the criminal histories of individuals at the time of sentencing to produce "race-neutral" predictions of re-arrest. In the process, we demonstrate that the most common approach to creating "race-neutral" models-- omitting race as a covariate-- still results in racially disparate predictions. We then demonstrate that the application of our proposed method to these data removes racial disparities from predictions with minimal impact on predictive accuracy.