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Evidence record 14870 · automatically gathered

Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups

arXiv:2607.27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview. This raises concerns beyond bias in AI: do LLMs grasp the emotional nuances conveyed via textual framing? In this work, we empirically evaluate how well an array of LLMs aligns with human emotional perception. Considering news headlines covering political and geopolitical conflicts, both human participants (n = 3011, a representative sample of

Record details

Published: 31 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 31 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups

arXiv cs.CL (ethics-relevant NLP) · 22 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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Shared topic: Bias & fairness · Shared topic: Safety & alignment

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arXiv fairness query · 25 July 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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arXiv fairness query · 25 July 2026

How to cite this record

ethics.ai (31 July 2026), “Sympathetic Framing: Evaluating AI Alignment across Sociodemographic Groups,” evidence record 14870, https://ethics.ai/record/14870 (originally published by arXiv cs.CY).

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