Inducing Group Fairness in LLM-Based Decisions

• URL: http://arxiv.org/abs/2406.16738v1
• Date: Mon, 24 Jun 2024 15:45:20 GMT
• Title: Inducing Group Fairness in LLM-Based Decisions
• Authors: James Atwood, Preethi Lahoti, Ananth Balashankar, Flavien Prost, Ahmad Beirami,
• Abstract summary: Group fairness in Prompting Large Language Models (LLMs) is a well-studied problem. We show that prompt-based classifiers may lead to unfair decisions. We introduce several remediation techniques and benchmark their fairness and performance trade-offs.
• Score: 12.368678951470162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompting Large Language Models (LLMs) has created new and interesting means for classifying textual data. While evaluating and remediating group fairness is a well-studied problem in classifier fairness literature, some classical approaches (e.g., regularization) do not carry over, and some new opportunities arise (e.g., prompt-based remediation). We measure fairness of LLM-based classifiers on a toxicity classification task, and empirically show that prompt-based classifiers may lead to unfair decisions. We introduce several remediation techniques and benchmark their fairness and performance trade-offs. We hope our work encourages more research on group fairness in LLM-based classifiers.

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