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Fairness of ChatGPT
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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.
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Cited by 1 Pith paper
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Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processing
A prompt-based pipeline lets closed LLMs like GPT-4o be used with classical group-fairness algorithms, without access to weights or embeddings.
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