mlsanityData to 7 Oct 2026

Fairness of ChatGPT

arXiv 2305.18569 ↗First posted 22 May 2023cs.LG cs.AI cs.CLcited by 19

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Abstract (arXiv)

Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment. However, there is a limited number of quantitative analyses and in-depth studies regarding fairness evaluations in LLMs, especially when applying LLMs to high-stakes fields. This work aims to fill this gap by providing a systematic evaluation of the effectiveness and fairness of LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's performance in high-takes fields including education, criminology, finance and healthcare. To conduct a thorough evaluation, we consider both group fairness and individual fairness metrics. We also observe the disparities in ChatGPT's outputs under a set of biased or unbiased prompts. This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible AI systems.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

20 of 31 references.

References · 20

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  4. 2017Counterfactual Fairnesscited by 802
  5. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  6. 2020Fairness in Machine Learning: A Surveycited by 369
  7. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  8. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  9. 2023OpenAGI: When LLM Meets Domain Expertscited by 196
  10. 2021A survey on datasets for fairness-aware machine learningcited by 157
  11. 2023Safety Assessment of Chinese Large Language Modelscited by 82
  12. 2022A Survey on Trustworthy Recommender Systemscited by 40
8 more
  1. 2023UP5: Unbiased Foundation Model for Fairness-aware Recommendationcited by 38
  2. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  3. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  4. 2023Ethical ChatGPT: Concerns, Challenges, and Commandmentscited by 13
  5. 2023Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processingcited by 11
  6. 2022Bias and unfairness in machine learning models: a systematic literature reviewcited by 7
  7. 2022Evaluation of group fairness measures in student performance prediction problemscited by 5
  8. 2023Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and financecited by 4

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Citing papers per year: 2023: 7; 2024: 8; 2025: 4.

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  1. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  2. 2023A Survey on Fairness in Large Language Modelscited by 91
  3. 2023VIP5: Towards Multimodal Foundation Models for Recommendationcited by 45
  4. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 29
  5. 2024Navigating LLM Ethics: Advancements, Challenges, and Future Directionscited by 28
  6. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  7. 2024CEB: Compositional Evaluation Benchmark for Fairness in Large Language Modelscited by 17
  8. 2023Fairness of ChatGPT and the Role Of Explainable-Guided Promptscited by 13
  9. 2024Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like Onecited by 9
  10. 2024No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Userscited by 9
  11. 2025FairCoder: Probing LLM Bias in High-Stakes Decision Making via Coding Taskscited by 9
  12. 2024Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systemscited by 7
7 more
  1. 2024Fairness in Large Language Models in Three Hourscited by 7
  2. 2024Fair In-Context Learning via Latent Concept Variablescited by 3
  3. 2024A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learningcited by 3
  4. 2025Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Modelscited by 2
  5. 2023Potential Societal Biases of ChatGPT in Higher Education: A Scoping Reviewcited by 1
  6. 2025Evaluating LLMs for Demographic-Targeted Social Bias Detection: A Comprehensive Benchmark Studycited by 1
  7. 2025Comparing Credit Risk Estimates in the Gen-AI Eracited by 0