The Mismeasure of Man and Models: Evaluating Allocational Harms in Large Language Models

• URL: http://arxiv.org/abs/2408.01285v1
• Date: Fri, 2 Aug 2024 14:13:06 GMT
• Title: The Mismeasure of Man and Models: Evaluating Allocational Harms in Large Language Models
• Authors: Hannah Chen, Yangfeng Ji, David Evans,
• Abstract summary: We introduce Rank-Allocation-Based Bias Index (RABBI), a model-agnostic bias measure that assesses potential allocational harms arising from biases in large language models (LLMs) Our results reveal that commonly-used bias metrics based on average performance gap and distribution distance fail to reliably capture group disparities in allocation outcomes. Our work highlights the need to account for how models are used in contexts with limited resource constraints.
• Score: 22.75594773147521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are now being considered and even deployed for applications that support high-stakes decision-making, such as recruitment and clinical decisions. While several methods have been proposed for measuring bias, there remains a gap between predictions, which are what the proposed methods consider, and how they are used to make decisions. In this work, we introduce Rank-Allocational-Based Bias Index (RABBI), a model-agnostic bias measure that assesses potential allocational harms arising from biases in LLM predictions. We compare RABBI and current bias metrics on two allocation decision tasks. We evaluate their predictive validity across ten LLMs and utility for model selection. Our results reveal that commonly-used bias metrics based on average performance gap and distribution distance fail to reliably capture group disparities in allocation outcomes, whereas RABBI exhibits a strong correlation with allocation disparities. Our work highlights the need to account for how models are used in contexts with limited resource constraints.

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