Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values

• URL: http://arxiv.org/abs/2502.00313v1
• Date: Sat, 01 Feb 2025 04:24:47 GMT
• Title: Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values
• Authors: Hadi Hosseini, Samarth Khanna,
• Abstract summary: A number of societal problems involve the distribution of resources, where fairness, along with economic efficiency, play a critical role in the desirability of outcomes.<n>This paper examines whether large language models (LLMs) adhere to fundamental fairness concepts and investigate their alignment with human preferences.
• Score: 13.798198972161657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing interest in employing large language models (LLMs) for decision-making in social and economic contexts has raised questions about their potential to function as agents in these domains. A significant number of societal problems involve the distribution of resources, where fairness, along with economic efficiency, play a critical role in the desirability of outcomes. In this paper, we examine whether LLM responses adhere to fundamental fairness concepts such as equitability, envy-freeness, and Rawlsian maximin, and investigate their alignment with human preferences. We evaluate the performance of several LLMs, providing a comparative benchmark of their ability to reflect these measures. Our results demonstrate a lack of alignment between current LLM responses and human distributional preferences. Moreover, LLMs are unable to utilize money as a transferable resource to mitigate inequality. Nonetheless, we demonstrate a stark contrast when (some) LLMs are tasked with selecting from a predefined menu of options rather than generating one. In addition, we analyze the robustness of LLM responses to variations in semantic factors (e.g. intentions or personas) or non-semantic prompting changes (e.g. templates or orderings). Finally, we highlight potential strategies aimed at enhancing the alignment of LLM behavior with well-established fairness concepts.

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