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Evidence record 7627 · automatically gathered

Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks

Large language models (LLMs) are increasingly used to make sense of ambiguous, open-textured, value-laden terms. Platforms routinely rely on LLMs for content moderation, asking them to label text based on disputed concepts like "hate speech" or "incitement"; hiring managers may use LLMs to rank who counts as "qualified"; and AI labs increasingly train models to self-regulate under constitutional-style ambiguous principles such as "biased" or "legitimate". This paper introduces ambiguity collapse

Record details

Published: 6 March 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation
Retrieved: 14 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.

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How to cite this record

ethics.ai (6 March 2026), “Ambiguity Collapse by LLMs: A Taxonomy of Epistemic Risks,” evidence record 7627, https://ethics.ai/record/7627 (originally published by arXiv).

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