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

Understand

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.