Fair Like Us? Auditing LLM Alignment in Resource Allocation

The increasing deployment of Large Language Models (LLMs) in decision-making, particularly concerning the allocation of scarce resources, introduces novel challenges regarding distributional justice. This research addresses the concern that LLM judgments may not adhere to established fairness frameworks and could violate normative principles. A general methodology has been developed to assess LLM fairness reasoning, comparing model responses directly with human judgments in identical scenarios.

The integration of LLMs into critical resource allocation decisions poses significant ethical and operational risks if their fairness judgments deviate from human expectations or normative principles. Understanding and mitigating these divergences is crucial for maintaining public trust, ensuring equitable outcomes, and preventing unintended societal consequences as AI adoption expands.

Large Language Models are increasingly integrated into decision-making processes, including the allocation of scarce, indivisible resources.

Forward consideration, not a verified fact.