Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts
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Computer Science > Computation and Language
arXiv:2608.29446 (cs)
[Submitted on 29 Aug 2026]
Title:Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts
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Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.29446 [cs.CL] |
| (or arXiv:2608.29446v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.29446
arXiv-issued DOI via DataCite (pending registration)
|
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View a PDF of the paper titled Whose Assessment of Distress? Community Perspectives and LLM Alignment on Well-Being Posts, by Andrew Aquilina and 2 other authors
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