LLM responses to moral judgment queries reinforce implicit humanization, potentially exacerbating overreliance and misplaced trust.
Matin, Gorav N
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A survey of 136 U.S. clinicians finds that autonomous AI prescribing would require confidence-based escalation, differentiated uncertainty communication, and inferential transparency to gain acceptance and properly allocate liability.
Analysis of lung function algorithms shows GLI-Global implicitly treats roughly 62% of the Black-White FEV1 gap as exposure-related and that clinical studies applied sufficiency-style fairness criteria before formal AI fairness work.
AI accuracy evaluation requires four normative choices on metrics, balancing, representative data, and thresholds that embed assumptions about risks and trade-offs, as analyzed through the EU AI Act.
citing papers explorer
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Implicit Humanization in Everyday LLM Moral Judgments
LLM responses to moral judgment queries reinforce implicit humanization, potentially exacerbating overreliance and misplaced trust.
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The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
A survey of 136 U.S. clinicians finds that autonomous AI prescribing would require confidence-based escalation, differentiated uncertainty communication, and inferential transparency to gain acceptance and properly allocate liability.
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What Medicine Taught Us About Fairness and What It Missed: Lessons from Reconsidering Race-Specific Lung Function Reference Algorithms
Analysis of lung function algorithms shows GLI-Global implicitly treats roughly 62% of the Black-White FEV1 gap as exposure-related and that clinical studies applied sufficiency-style fairness criteria before formal AI fairness work.
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Is your AI Model Accurate Enough? The Difficult Choices Behind Rigorous AI Development and the EU AI Act
AI accuracy evaluation requires four normative choices on metrics, balancing, representative data, and thresholds that embed assumptions about risks and trade-offs, as analyzed through the EU AI Act.