mlsanityData to 7 Oct 2026

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

arXiv 2202.08176 ↗First posted 16 Feb 2022cs.LG cs.AIcited by 7

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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.

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

Builds on

17 of 86 references.

References · 17

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2018Model Cards for Model Reportingcited by 1,068
  4. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  5. 2021A survey on datasets for fairness-aware machine learningcited by 157
  6. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  7. 2018The Frontiers of Fairness in Machine Learningcited by 139
  8. 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
  9. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  10. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  11. 2021Fairness via Representation Neutralizationcited by 63
  12. 2021Fairness for Image Generation with Uncertain Sensitive Attributescited by 31
5 more
  1. 2019A Causal Bayesian Networks Viewpoint on Fairnesscited by 19
  2. 2019Fair Adversarial Gradient Tree Boostingcited by 17
  3. 2019Dynamic fairness - Breaking vicious cycles in automatic decision makingcited by 4
  4. 2019Multi-Differential Fairness Auditor for Black Box Classifierscited by 3
  5. 2019Analyzing Bias in Sensitive Personal Information Used to Train Financial Modelscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 1.

7 papers.

  1. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  2. 2023Fairness of ChatGPTcited by 19
  3. 2025From Hazard Identification to Controller Design: Proactive and LLM-Supported Safety Engineering for ML-Powered Systemscited by 7
  4. 2024Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimalitycited by 2
  5. 2023Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projectscited by 1
  6. 2024Equity in Healthcare: Analyzing Disparities in Machine Learning Predictions of Diabetic Patient Readmissionscited by 0
  7. 2024Speculations on Uncertainty and Humane Algorithmscited by 0