Proxy reliance in large language model decisions is uncalibrated to predictive evidence

2026-08-24

AI Summary

研究通过测量四个大型语言模型在临床排名任务中的因果代理效应，解决了代理依赖与预测证据不匹配的问题。

Abstract

Large language models (LLMs) are entering decisions in triage and lending, where task-relevant inference must be distinguished from impermissible proxy use. Current audits ask whether decisions change when demographics change. But attributes correlated with a protected group carry predictive value, so a changed decision can be discrimination or sound inference. We measure causal proxy effects in four LLMs on a clinical-ranking task with known ground truth, where the reliance the evidence warrants can be computed exactly and used as the reference. One audit signal yields three verdicts: over-reliance, warranted and under-reliance. Under neutral labels every model relies on proxies with no information. Informative proxies draw all three. Social field names push reliance down, below the reference in one model. Two findings explain this. Reliance severely undertracks the evidence, and social-label suppression is fragile, since in-context examples raise it above zero in every model. Accuracy-based evaluation detects none of this.

Problem

• large language models
• proxy reliance
• predictive evidence
• decision making
• clinical-ranking task

Innovation

• causal proxy effects
• clinical-ranking task
• evidence reliance
• social-label suppression
• in-context examples