Empirical study of 522 registered data brokers finds 9% fully compliant with Delete Act transparency requirements, 43% make exercising all privacy rights impossible, and 64% add substantial friction to request processes.
Dangers of LLM therapists: Stigma and safety in AI-driven mental health
5 Pith papers cite this work. Polarity classification is still indexing.
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The authors built and expert-evaluated an agentic AI system integrating DEA regulatory data with dynamic scientific literature via RAG to provide accurate, context-sensitive substance use education, with mean Likert ratings of 4.18-4.35 and substantial rater agreement.
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
citing papers explorer
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Privacy Without Remedy: An Assessment of Data Broker Compliance with California Privacy Law
Empirical study of 522 registered data brokers finds 9% fully compliant with Delete Act transparency requirements, 43% make exercising all privacy rights impossible, and 64% add substantial friction to request processes.
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Agentic AI for Substance Use Education: Integrating Regulatory and Scientific Knowledge Sources
The authors built and expert-evaluated an agentic AI system integrating DEA regulatory data with dynamic scientific literature via RAG to provide accurate, context-sensitive substance use education, with mean Likert ratings of 4.18-4.35 and substantial rater agreement.
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The Quiet Path from Seemingly Minor Design Errors to Workplace AI Incidents
Empirical analysis of 1,524 AI incident reports shows 83% arise from worker-AI trait misalignments, with 74% of those traceable to developers prioritizing efficiency over precision or personalization.
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AI Washing Inflates Expected Performance but Not Interaction Outcomes: An AI Placebo Study Using Fitts' Law
AI-labeled input devices raise user performance expectations but produce no measurable change in objective or subjective interaction outcomes.
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.