First-Person Fairness in Chatbots

TL;DR

This paper introduces a scalable counterfactual method using a Language Model as a Research Assistant to evaluate and analyze demographic biases in chatbot responses across diverse tasks and domains, highlighting the effectiveness of reinforcement learning in bias mitigation.

Contribution

It presents the first large-scale, real-world chatbot fairness evaluation framework employing LMRA for bias detection and demonstrates the impact of reinforcement learning on reducing biases.

Findings

Biases vary across demographics and tasks

Human annotations validate LMRA bias assessments

Reinforcement learning reduces detected biases

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.