聊天机器人中的第一人称公平性
First-Person Fairness in Chatbots
AI总结：
本文提出一种基于反事实和语言模型研究助手（LMRA）的可扩展方法，用于评估聊天机器人针对用户人口统计学特征的“第一人称公平性”，并在数百万次真实交互中验证了其有效性，发现训练后强化学习能显著缓解偏见。
摘要：
鉴于聊天机器人的快速普及，评估其公平性至关重要，然而典型的聊天机器人任务（如简历撰写、娱乐）不同于传统的算法公平性讨论核心——机构决策任务（如简历筛选）。聊天机器人的开放性和多样化用例需要新的偏见评估方法。本文通过引入一种可扩展的反事实方法来评估“第一人称公平性”（即基于人口统计学特征对聊天机器人用户的公平性）以应对这些挑战。我们的方法采用语言模型作为研究助手（LMRA），以生成对有害刻板印象的定量测量和对聊天机器人回复中人口统计学差异的定性分析。我们应用该方法评估了六个语言模型在数百万次交互中的偏见，涵盖九个领域的六十六项任务，涉及两种性别和四个种族。独立的人工标注证实了 LMRA 生成的偏见评估。本研究是首次基于真实世界聊天数据的大规模公平性评估。我们强调，训练后强化学习技术能显著缓解这些偏见。该评估为持续的偏见监测和缓解提供了实用方法。
英文摘要：
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.
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