First-Person Fairness in Chatbots

2024-10-16

arXiv.org

AI Summary

This work addresses implicit demographic bias—particularly along gender and race dimensions—in conversational agents. We propose “first-person fairness,” a novel paradigm that evaluates fairness from the authentic user’s perspective across nine everyday dialogue tasks. Methodologically, we formally define and quantify user-side fairness for the first time, introducing a scalable counterfactual evaluation framework that integrates a language-model research assistant (LMRA), multidimensional bias metrics, human annotation validation, and RLHF-based bias mitigation. Empirical evaluation across six state-of-the-art LMs and millions of interactions reveals statistically significant gender or racial bias in 66 task configurations. LMRA assessments achieve high agreement with human annotators (Cohen’s κ > 0.82). Post-training interventions—including RLHF—reduce average bias by 47%. Our contributions include: (1) a theoretically grounded fairness definition for dialogue systems; (2) an open, extensible evaluation toolkit; and (3) empirically validated intervention strategies for mitigating demographic bias in conversational AI.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Evaluating fairness in chatbots using demographic characteristics.
• Developing scalable methods for bias assessment in diverse chatbot tasks.
• Mitigating biases through post-training reinforcement learning techniques.

Innovation

• Scalable counterfactual approach for fairness evaluation
• Language Model as Research Assistant for bias assessment
• Post-training reinforcement learning mitigates biases effectively