arXiv:2606.30412v1 Announce Type: cross Abstract: From housing allocation for households experiencing homelessness to triage in emergency departments, LLMs are increasingly being considered as judges of consequential decisions that require ranking people for scarce resources. Ranking large groups simultaneously is cognitively demanding and error-prone. A natural solution, drawing on decades of social choice theory, elicits pairwise comparisons and aggregates them into a total order. However, a fundamental question remains when LLMs serve as the pairwise judge: how can a practitioner tell, befo
The increasing consideration of LLMs for high-stakes societal decisions, coupled with the inherent complexity of ranking large groups, necessitates understanding their reliability in such tasks.
As LLMs are proposed for consequential allocation of scarce resources, their ability to make fair and effective judgments is critical for trust and societal functioning.
The focus is shifting from general LLM capabilities to their specific performance and evaluability in complex, qualitative ranking scenarios with real-world impact.
- · AI ethics researchers
- · Social choice theorists
- · Organizations developing robust LLM evaluation techniques
- · Regulatory bodies in critical resource allocation
- · Uncritically deployed LLM systems
- · Institutions relying solely on single-instance LLM outputs for ranking
- · Developers neglecting evaluation frameworks
This research provides a framework for evaluating LLM performance in ranking scenarios, particularly for resource allocation.
Improved understanding of LLM reliability in ranking could lead to their cautious adoption in some but not all high-stakes decision-making processes.
Societal trust in AI-driven resource allocation could be either fortified or eroded based on the ability to demonstrate and ensure fairness and transparency in such systems.
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Read at arXiv cs.AI