Generative models privatize social relations by automating social capacities into synthetic forms owned by private companies.
The Canadian Journal of Psychiatry64(7), 456–464 (2019)
4 Pith papers cite this work, alongside 984 external citations. Polarity classification is still indexing.
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PULSE demonstrates that agentic LLM-based investigation of passive smartphone sensing data achieves balanced accuracies of 0.743 (with diary) and 0.713 (sensing-only) for predicting emotion regulation desire and intervention availability in 50 cancer survivors.
SLIP and ETHICS introduce a staged intervention system for AI emotional companions using qualitative affect and narrative signals, with small-scale deployment and synthetic tests showing zero false positives for normal use but detection gaps in sustained high-energy states.
Introduces a clean matched benchmark and Dynamic Emotional Signature Graphs (DESG) framework that detects implicit sycophancy via clinical-state transitions and reports a 0.0488 macro-F1 gain over baselines on harmful-risk detection.
citing papers explorer
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Synthetic Sociality: How Generative Models Privatize the Social Fabric
Generative models privatize social relations by automating social capacities into synthetic forms owned by private companies.
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PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship
PULSE demonstrates that agentic LLM-based investigation of passive smartphone sensing data achieves balanced accuracies of 0.743 (with diary) and 0.713 (sensing-only) for predicting emotion regulation desire and intervention availability in 50 cancer survivors.
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SLIP & ETHICS: Graduated Intervention for AI Emotional Companions
SLIP and ETHICS introduce a staged intervention system for AI emotional companions using qualitative affect and narrative signals, with small-scale deployment and synthetic tests showing zero false positives for normal use but detection gaps in sustained high-energy states.
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Auditing Stealth Sycophancy in Mental-Health Dialogue: Structured Clinical-State Diagnostics and Clean Matched Benchmarks
Introduces a clean matched benchmark and Dynamic Emotional Signature Graphs (DESG) framework that detects implicit sycophancy via clinical-state transitions and reports a 0.0488 macro-F1 gain over baselines on harmful-risk detection.