Cross-cultural survey of 4,641 participants shows LLM emotional support adoption varies widely by country and demographics, with socioeconomic status as strongest predictor of trust and use, and English-speaking nations more accepting than others in Europe.
The ethics of advanced ai assistants, 2024
19 Pith papers cite this work. Polarity classification is still indexing.
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The conceptual multiverse system with a verification framework for decision structures helps users in philosophy, AI alignment, and poetry build clearer working maps of open-ended problems by making implicit LLM choices explicit and changeable.
Crowdsourced metaphors show rising anthropomorphism and warmth toward AI that predict trust and adoption, with notable demographic differences.
LLMs prompted as peer supporters for ADRD caregivers produce synthetic lived experience through narrative language that differs from human peers in first-person and past-tense usage, revealing a narrative authenticity gap.
LLMs produce interpretive closure in 87.5% of ambiguous social scenarios through narrative alignment, reversal, or normative advice, with first-person perspectives increasing alignment tendencies.
AI agents on Moltbook reflect the specific behavioral traits of their linked human owners across multiple dimensions, with stronger transfer linked to greater privacy risks.
The 2025 AI Agent Index catalogs technical and safety details for 30 deployed AI agents and finds low developer transparency on safety, evaluations, and societal impacts.
Introduces the concept of agentic inequality and develops a three-dimensional framework (availability, quality, quantity) to analyze how autonomous AI agents could deepen or mitigate existing divides through scalable goal delegation.
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
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.
HBHC protocol binds hierarchical credentials to heartbeat proofs for deterministic bounded-time revocation in AI agent swarms without network round-trips.
Proposes cryptographic certificates of validity by translating logical policy predicates into succinct proof systems for verifying AI agent actions.
State-of-the-art LLMs respond inconsistently to queries from protected-group personas, with some responses omitting key information that should be provided.
Proposes framing auditing of deployed AI systems as continuous statistical monitoring of risk-controlled constraints like fairness and safety under uncertainty.
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.
TSAssistant decomposes target safety assessment report generation into research and synthesis subagents with tool-based evidence retrieval, hierarchical instructions, and interactive human refinement, reporting high reproducibility and grounding.
The agentic web requires new normative infrastructure of laws, norms, and practices to allow user-delegated AI agents to access online properties without being blocked as malicious bots.
AGI may arrive by 2030-2040 and reshape global power balances, requiring Europe to close gaps in compute, talent retention, industrial adoption, and unified policy responses through a coordinated preparedness agenda.
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Responsible Agentic AI Requires Explicit Provenance
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.