mlsanityData to 7 Oct 2026

Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies

arXiv 2304.07683 ↗First posted 16 Apr 2023cs.CYcited by 83

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Abstract (arXiv)

The significant advancements in applying Artificial Intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey paper offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases - highlighting the emergent issue of generative AI bias where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discussing the ethical considerations of their implementation and emphasizing the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 26; 2025: 36; 2026: 15.

83 papers.

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  1. 2023A Survey on Fairness in Large Language Modelscited by 91
  2. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  3. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
  4. 2024On the Challenges and Opportunities in Generative AIcited by 31
  5. 2024A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directionscited by 27
  6. 2025The More You Automate, the Less You See: Hidden Pitfalls of AI Scientist Systemscited by 24
  7. 2024FAIntbench: A Holistic and Precise Benchmark for Bias Evaluation in Text-to-Image Modelscited by 21
  8. 2024Mitigating the Risk of Health Inequity Exacerbated by Large Language Modelscited by 21
  9. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  10. 2023Automated Claim Matching with Large Language Models: Empowering Fact-Checkers in the Fight Against Misinformationcited by 18
  11. 2024FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applicationscited by 15
  12. 2025When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMscited by 14
38 more
  1. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  2. 2025FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theorycited by 13
  3. 2025Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeekcited by 11
  4. 2024Representation Bias in Political Sample Simulations with Large Language Modelscited by 10
  5. 2024Statistical inference on black-box generative models in the data kernel perspective spacecited by 9
  6. 2024Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancementcited by 7
  7. 2025Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Taskscited by 7
  8. 2023Ethical Framework for Harnessing the Power of AI in Healthcare and Beyondcited by 6
  9. 2025Faces of Fairness: Examining Bias in Facial Expression Recognition Datasets and Modelscited by 6
  10. 2025Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impactscited by 6
  11. 2025No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biasescited by 6
  12. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  13. 2024Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selectioncited by 5
  14. 2024Less can be more: representational vs. stereotypical gender bias in facial expression recognitioncited by 5
  15. 2025Position: Bridge the Gaps between Machine Unlearning and AI Regulationcited by 5
  16. 2025Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Modelcited by 5
  17. 2025Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studiescited by 4
  18. 2025BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agentscited by 4
  19. 2026The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truthcited by 4
  20. 2023Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360cited by 3
  21. 2024Evaluating AI fairness in credit scoring with the BRIO toolcited by 3
  22. 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Biascited by 3
  23. 2025Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Carecited by 3
  24. 2025Enhancements for Developing a Comprehensive AI Fairness Assessment Standardcited by 3
  25. 2024Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIscited by 2
  26. 2024Rolling in the deep of cognitive and AI biasescited by 2
  27. 2024Isolated Causal Effects of Natural Languagecited by 2
  28. 2024Developer Perspectives on Licensing and Copyright Issues Arising from Generative AI for Software Developmentcited by 2
  29. 2025The Pitfalls of "Security by Obscurity" And What They Mean for Transparent AIcited by 2
  30. 2025Breaking Down Bias: On The Limits of Generalizable Pruning Strategiescited by 2
  31. 2025Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigationcited by 2
  32. 2025Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detectioncited by 2
  33. 2024Bias in Text Embedding Modelscited by 1
  34. 2024Generating Fearful Images: Investigating Potential Emotional Biases in Image-Generation Modelscited by 1
  35. 2025Automated Bias Assessment in AI-Generated Educational Content Using CEAT Frameworkcited by 1
  36. 2025The Role of Generative AI in Facilitating Social Interactions: A Scoping Reviewcited by 1
  37. 2025Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGIcited by 1
  38. 2025Are generative AI text annotations systematically biased?cited by 1

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