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Evidence record 394 · automatically gathered

Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues

As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behaviors are genuine or superficial. We show that current fairness evaluations substantially overestimate moral safety. Models appear fair when demographic identity is stated as an explicit label, yet become measurably less fair when the same identity must be inferred. We term this failure performative compliance, where a model is fair when the present

Record details

Published: 30 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Healthcare
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.

Shared topic: Bias & fairness · Shared topic: Regulation

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Shared topic: Bias & fairness · Shared topic: Regulation

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Shared topic: Bias & fairness · Shared topic: Regulation

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How to cite this record

ethics.ai (30 June 2026), “Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues,” evidence record 394, https://ethics.ai/record/394 (originally published by arXiv).

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