谁来评估心理困扰？社区视角与大语言模型在福祉帖子上的对齐

中文摘要 AI 辅助

对心理困扰的判断具有社会情境性：何种情况属于令人担忧的范畴，取决于围绕情感表达、脆弱性与求助行为的社区规范。然而，用于心理困扰检测的大语言模型（LLM）通常仅对齐单一、无差异的标准。这些模型在多大程度上能捕捉其所评估语言所属社区的视角？我们通过一项视角主义标注研究来解决该问题：321名参与者对来自6个基于身份的社区的1198条Reddit帖子作出9587次判断，生成了社区特定标签。处于情境化内群体条件下的评分者，与未情境化外群体评分者相比，表现出适度的更倾向于与自身社区达成一致的趋势（优势比OR=1.18），该效应在不同社区间存在显著差异。随后，我们针对这些标签评估了9种开放权重LLM配置与4种前沿配置。开放权重LLM系统性地高估了心理困扰：当社区感知到无至轻度困扰时，这些模型仅达到31%-44%的准确率，且主要产生假阳性结果。GPT-5与Gemini 2.5 Pro即便在其全样本高估/低估率混合的情况下，仍表现出相同的无至轻度困扰高估倾向，而Claude Opus 4则更为保守。该模式并非简单反映局外人的阅读立场：未情境化外群体人类的整体判断近乎对称，高估率为18%，低估率为19%。相反，那些高估无至轻度案例的模型，其心理困扰先验超过了情境化内群体与未情境化外群体的人类判断。这些发现对心理健康情境下的公平AI部署具有启示意义：校准不当的心理困扰检测可能会对被评估的社区产生不均等的影响。

英文摘要

Judgments about psychological distress are socially situated: what counts as concerning hinges on community norms around emotional expression, vulnerability, and help-seeking. Yet large language models (LLMs) used for distress detection are typically aligned to a single, undifferentiated standard. How well do these models capture the perspectives of the communities whose language they assess? We address this question through a perspectivist annotation study in which 321 participants provided 9,587 judgments on 1,198 Reddit posts spanning six identity-based communities, yielding community-specific labels. Raters in the contextualized in-group condition show a modest tendency to agree more with their community than uncontextualized out-group raters (OR = 1.18), an effect varying significantly across communities. We then evaluate nine open-weight LLM configurations and four frontier configurations against these labels. Open-weight LLMs systematically over-estimate distress: when communities perceive none-to-mild distress, these models achieve only 31-44% accuracy, predominantly producing false positives. GPT-5 and Gemini 2.5 Pro show the same none-to-mild inflation even when their full-sample over/under rates are mixed, while Claude Opus 4 is more conservative. This pattern does not simply mirror an outsider reading position: uncontextualized out-group human aggregates were nearly symmetric, with 18% over-estimation versus 19% under-estimation. Instead, the models that inflate none-to-mild cases exhibit a distress prior that exceeds both contextualized in-group and uncontextualized out-group human judgments. These findings have implications for equitable AI deployment in mental health contexts, where miscalibrated distress detection may unevenly affect the communities being assessed.

发表机构

• School of Computing and Information, University of Pittsburgh（匹兹堡大学计算与信息学院）

机构由 AI 辅助整理，请以论文原文为准。