# Bias and unfairness in machine learning models: a systematic literature review

arXiv: https://arxiv.org/abs/2202.08176 · HTML: https://mlsanity.com/paper/2202.08176/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 86 references that are in the map.
- First posted on arXiv 16 Feb 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://mlsanity.com/paper/2008.05122/) (2020)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Fairness in Credit Scoring: Assessment, Implementation and Profit Implications](https://mlsanity.com/paper/2103.01907/) (2021)
- [Fairness via Representation Neutralization](https://mlsanity.com/paper/2106.12674/) (2021)
- [Fairness for Image Generation with Uncertain Sensitive Attributes](https://mlsanity.com/paper/2106.12182/) (2021)
- [A Causal Bayesian Networks Viewpoint on Fairness](https://mlsanity.com/paper/1907.06430/) (2019)
- [Fair Adversarial Gradient Tree Boosting](https://mlsanity.com/paper/1911.05369/) (2019)
- [Dynamic fairness - Breaking vicious cycles in automatic decision making](https://mlsanity.com/paper/1902.00375/) (2019)
- [Multi-Differential Fairness Auditor for Black Box Classifiers](https://mlsanity.com/paper/1903.07609/) (2019)
- [Analyzing Bias in Sensitive Personal Information Used to Train Financial Models](https://mlsanity.com/paper/1911.03623/) (2019)

## Built on by (most cited first, 7 of 7)

- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Fairness of ChatGPT](https://mlsanity.com/paper/2305.18569/) (2023)
- [From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systems](https://mlsanity.com/paper/2502.07974/) (2025)
- [Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality](https://mlsanity.com/paper/2404.08230/) (2024)
- [Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects](https://mlsanity.com/paper/2311.14214/) (2023)
- [Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissions](https://mlsanity.com/paper/2403.19057/) (2024)
- [Speculations on Uncertainty and Humane Algorithms](https://mlsanity.com/paper/2408.06736/) (2024)

## Abstract (arXiv)

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learning models, identifying mitigation methods, fairness metrics, and supporting tools. A Systematic Literature Review found 40 eligible articles published between 2017 and 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases. The results show numerous bias and unfairness detection and mitigation approaches for ML technologies, with clearly defined metrics in the literature, and varied metrics can be highlighted. We recommend further research to define the techniques and metrics that should be employed in each case to standardize and ensure the impartiality of the machine learning model, thus, allowing the most appropriate metric to detect bias and unfairness in a given context.