First-Person Fairness in Chatbots
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arXiv
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| Format: | Preprint |
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2024
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| author | Eloundou, Tyna Beutel, Alex Robinson, David G. Gu-Lemberg, Keren Brakman, Anna-Luisa Mishkin, Pamela Shah, Meghan Heidecke, Johannes Weng, Lilian Kalai, Adam Tauman |
| author_facet | Eloundou, Tyna Beutel, Alex Robinson, David G. Gu-Lemberg, Keren Brakman, Anna-Luisa Mishkin, Pamela Shah, Meghan Heidecke, Johannes Weng, Lilian Kalai, Adam Tauman |
| contents | Evaluating chatbot fairness is crucial given their rapid proliferation, yet typical chatbot tasks (e.g., resume writing, entertainment) diverge from the institutional decision-making tasks (e.g., resume screening) which have traditionally been central to discussion of algorithmic fairness. The open-ended nature and diverse use-cases of chatbots necessitate novel methods for bias assessment. This paper addresses these challenges by introducing a scalable counterfactual approach to evaluate "first-person fairness," meaning fairness toward chatbot users based on demographic characteristics. Our method employs a Language Model as a Research Assistant (LMRA) to yield quantitative measures of harmful stereotypes and qualitative analyses of demographic differences in chatbot responses. We apply this approach to assess biases in six of our language models across millions of interactions, covering sixty-six tasks in nine domains and spanning two genders and four races. Independent human annotations corroborate the LMRA-generated bias evaluations. This study represents the first large-scale fairness evaluation based on real-world chat data. We highlight that post-training reinforcement learning techniques significantly mitigate these biases. This evaluation provides a practical methodology for ongoing bias monitoring and mitigation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | First-Person Fairness in Chatbots Eloundou, Tyna Beutel, Alex Robinson, David G. Gu-Lemberg, Keren Brakman, Anna-Luisa Mishkin, Pamela Shah, Meghan Heidecke, Johannes Weng, Lilian Kalai, Adam Tauman Computers and Society Artificial Intelligence Computation and Language Evaluating chatbot fairness is crucial given their rapid proliferation, yet typical chatbot tasks (e.g., resume writing, entertainment) diverge from the institutional decision-making tasks (e.g., resume screening) which have traditionally been central to discussion of algorithmic fairness. The open-ended nature and diverse use-cases of chatbots necessitate novel methods for bias assessment. This paper addresses these challenges by introducing a scalable counterfactual approach to evaluate "first-person fairness," meaning fairness toward chatbot users based on demographic characteristics. Our method employs a Language Model as a Research Assistant (LMRA) to yield quantitative measures of harmful stereotypes and qualitative analyses of demographic differences in chatbot responses. We apply this approach to assess biases in six of our language models across millions of interactions, covering sixty-six tasks in nine domains and spanning two genders and four races. Independent human annotations corroborate the LMRA-generated bias evaluations. This study represents the first large-scale fairness evaluation based on real-world chat data. We highlight that post-training reinforcement learning techniques significantly mitigate these biases. This evaluation provides a practical methodology for ongoing bias monitoring and mitigation. |
| title | First-Person Fairness in Chatbots |
| topic | Computers and Society Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.19803 |