mlsanityData to 7 Oct 2026

The Pursuit of Fairness in Artificial Intelligence Models: A Survey

arXiv 2403.17333 ↗First posted 26 Mar 2024cs.AI cs.CY cs.LGcited by 13

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Abstract (arXiv)

Artificial Intelligence (AI) models are now being utilized in all facets of our lives such as healthcare, education and employment. Since they are used in numerous sensitive environments and make decisions that can be life altering, potential biased outcomes are a pressing matter. Developers should ensure that such models don't manifest any unexpected discriminatory practices like partiality for certain genders, ethnicities or disabled people. With the ubiquitous dissemination of AI systems, researchers and practitioners are becoming more aware of unfair models and are bound to mitigate bias in them. Significant research has been conducted in addressing such issues to ensure models don't intentionally or unintentionally perpetuate bias. This survey offers a synopsis of the different ways researchers have promoted fairness in AI systems. We explore the different definitions of fairness existing in the current literature. We create a comprehensive taxonomy by categorizing different types of bias and investigate cases of biased AI in different application domains. A thorough study is conducted of the approaches and techniques employed by researchers to mitigate bias in AI models. Moreover, we also delve into the impact of biased models on user experience and the ethical considerations to contemplate when developing and deploying such models. We hope this survey helps researchers and practitioners understand the intricate details of fairness and bias in AI systems. By sharing this thorough survey, we aim to promote additional discourse in the domain of equitable and responsible AI.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 82 of its 212 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

82 of 212 references.

References · 82

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  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
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38 more
  1. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  2. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  3. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  4. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
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  8. 2018Path-Specific Counterfactual Fairnesscited by 179
  9. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 170
  10. 2017A Convex Framework for Fair Regressioncited by 147
  11. 2019Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learningcited by 145
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  14. 2020To be Robust or to be Fair: Towards Fairness in Adversarial Trainingcited by 137
  15. 2019End-to-End Bias Mitigation by Modelling Biases in Corporacited by 127
  16. 2018An Intersectional Definition of Fairnesscited by 126
  17. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
  18. 2017Fairness Testing: Testing Software for Discriminationcited by 123
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Citing papers per year: 2024: 5; 2025: 7; 2026: 1.

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  1. 2024Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmerscited by 5
  2. 2024How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neuronscited by 3
  3. 2025Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairnesscited by 3
  4. 2024Rolling in the deep of cognitive and AI biasescited by 2
  5. 2024INFELM: In-depth Fairness Evaluation of Large Text-To-Image Modelscited by 1
  6. 2025ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for Detection of Bias, Discrimination and Stereotypingcited by 1
  7. 2025Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanationscited by 1
  8. 2025ECHO: A Participatory Framework for Bias-Anchored AI Harm Anticipationcited by 1
  9. 2026Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Modelscited by 1
  10. 2024Revisiting Technical Bias Mitigation Strategiescited by 0
  11. 2025Testing Individual Fairness in Graph Neural Networkscited by 0
  12. 2025Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systemscited by 0
1 more
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