# fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation

arXiv: https://arxiv.org/abs/2104.00507 · HTML: https://mlsanity.com/paper/2104.00507/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 16 of 16)

- [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)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [On the Apparent Conflict Between Individual and Group Fairness](https://mlsanity.com/paper/1912.06883/) (2019)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making](https://mlsanity.com/paper/1806.01059/) (2018)
- [Fair Data Adaptation with Quantile Preservation](https://mlsanity.com/paper/1911.06685/) (2019)
- [DALEX: explainers for complex predictive models](https://mlsanity.com/paper/1806.08915/) (2018)
- [Fairness Measures for Regression via Probabilistic Classification](https://mlsanity.com/paper/2001.06089/) (2020)
- [Debiasing classifiers: is reality at variance with expectation?](https://mlsanity.com/paper/2011.02407/) (2020)

## Built on by (most cited first, 9 of 9)

- [fairadapt: Causal Reasoning for Fair Data Pre-processing](https://mlsanity.com/paper/2110.10200/) (2021)
- [fairml: A Statistician's Take on Fair Machine Learning Modelling](https://mlsanity.com/paper/2305.02009/) (2023)
- [Causal Fair Machine Learning via Rank-Preserving Interventional Distributions](https://mlsanity.com/paper/2307.12797/) (2023)
- [FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models](https://mlsanity.com/paper/2604.04858/) (2026)
- [Fairmetrics: An R package for group fairness evaluation](https://mlsanity.com/paper/2506.06243/) (2025)
- [Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis](https://mlsanity.com/paper/2307.06871/) (2023)
- [dsld: A Socially Relevant Tool for Teaching Statistics](https://mlsanity.com/paper/2411.04228/) (2024)
- [seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness](https://mlsanity.com/paper/2504.08418/) (2025)
- [FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness](https://mlsanity.com/paper/2607.08953/) (2026)

## Abstract (arXiv)

Machine learning decision systems are getting omnipresent in our lives. From dating apps to rating loan seekers, algorithms affect both our well-being and future. Typically, however, these systems are not infallible. Moreover, complex predictive models are really eager to learn social biases present in historical data that can lead to increasing discrimination. If we want to create models responsibly then we need tools for in-depth validation of models also from the perspective of potential discrimination. This article introduces an R package fairmodels that helps to validate fairness and eliminate bias in classification models in an easy and flexible fashion. The fairmodels package offers a model-agnostic approach to bias detection, visualization and mitigation. The implemented set of functions and fairness metrics enables model fairness validation from different perspectives. The package includes a series of methods for bias mitigation that aim to diminish the discrimination in the model. The package is designed not only to examine a single model, but also to facilitate comparisons between multiple models.