{"total":12,"items":[{"citing_arxiv_id":"2606.31755","ref_index":119,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Technical Typology of AI Systems in Public Administration","primary_cat":"cs.CY","submitted_at":"2026-06-30T14:44:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper defines five AI system categories for public administration and reports that 55% of 91 recent papers leave the system type underspecified while 31% study one type but motivate with another.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23491","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hallucinations in Organization-backed AI advisors: Evidence about Skepticism, Verification, and Reliance in Goal-Directed Use","primary_cat":"cs.HC","submitted_at":"2026-06-22T15:36:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Literature review synthesizing evidence on user skepticism, verification, and reliance with hallucinating AI advisors, noting that output-related cues like warnings show weak effects and that content category has not been experimentally varied.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13658","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Before You Think: System 0, AI-Mediated Cognition and Cognitive Colonization","primary_cat":"cs.AI","submitted_at":"2026-06-11T17:54:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"System 0 is positioned as theoretically distinct from Tri-System Theory and Thinkframes, with cognitive colonization as a key mechanism of invisible AI influence on cognition.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05330","ref_index":110,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing","primary_cat":"cs.CL","submitted_at":"2026-06-03T18:17:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"PERSUASIONTRACE introduces a Bayesian-network simulated target for multi-turn persuasion that matches human belief dynamics (81 vs 80) better than LLM baselines (64) and enables process-level evaluation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23867","ref_index":44,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Human Decision-Making with Persuasive and Narrative LLM Explanations","primary_cat":"cs.HC","submitted_at":"2026-05-22T17:25:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"LLM narrative explanations of varying persuasiveness did not improve human decision accuracy over AI predictions alone but increased reliance on AI even when incorrect.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12772","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Just Ask for a Table: A Thirty-Token User Prompt Defeats Sponsored Recommendations in Twelve LLMs","primary_cat":"cs.CV","submitted_at":"2026-05-12T21:34:33+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A 30-token prompt requesting a neutral comparison table cuts sponsored recommendations in LLMs from roughly 50% to near zero.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10440","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"TourMart: A Parametric Audit Instrument for Commission Steering in LLM Travel Agents","primary_cat":"cs.CY","submitted_at":"2026-05-11T12:11:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TourMart quantifies commission steering in LLM travel agents via paired counterfactual prompts, reporting 3.5-7.7 percentage point increases in steered recommendations for tested models.","context_count":1,"top_context_role":"extension","top_context_polarity":"extend","context_text":"'s silicon-sample framework (Political Analysis 2023) [12] es- tablish that LLMs can be treated as behavioral subjects in social-scientific simulation. We inherit the legitimacy of LLM agents as traveler-reader and OTA-producer stand-ins, not their memory, planning, or fidelity-validation architectures. (L2, load-bearing) LLM persuasion measurement.Salvi et al. (Na- ture Human Behaviour 2025) [13] and the concurrent commercial-steering line [14, 15] supply the paired-counterfactual measurement frame that Tour- Mart uses to identify message-induced steering. We extend it from a binary disclosure manipulation to a continuous(λ, κ)governance grid acting on a frozen welfare rule. (L3, load-bearing) Algorithmic platform audit.Sandvig et al. (ICA 2014) [16], Hannak et al."},{"citing_arxiv_id":"2604.07813","ref_index":54,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Agentivism: a learning theory for the age of artificial intelligence","primary_cat":"cs.AI","submitted_at":"2026-04-09T05:09:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.","context_count":1,"top_context_role":"other","top_context_polarity":"support","context_text":"Whether AI-generated guidance is epistemically trustworthy; interaction alone does not guarantee justified learning [53]. ConnectivismWhy learning depends on distributed networks of people, tools, and information resources [8]. What changes when network nodes become gen- erative, persuasive, and partially agentic, shaping representation, priorities, and action in real time [54, 55]. interactional position of a knowledgeable other without satisfying the epistemic con- ditions that make such guidance trustworthy [53]. Conversational participation alone is therefore no guarantee of justified learning. Connectivism remains highly relevant because knowledge is still distributed across networks of people, tools, and information resources [8]."},{"citing_arxiv_id":"2604.02585","ref_index":23,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Mitigating LLM biases toward spurious social contexts using direct preference optimization","primary_cat":"cs.AI","submitted_at":"2026-04-02T23:42:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Debiasing-DPO reduces bias to spurious social contexts by 84% and improves predictive accuracy by 52% on average for LLMs evaluating U.S. classroom transcripts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19787","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans","primary_cat":"cs.CL","submitted_at":"2026-03-31T19:27:59+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Zero-shot LLM agents with human personas predict individual social media reactions better than chance (MCC 0.29) but worse than conventional text classifiers (MCC 0.36).","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.04003","ref_index":64,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making","primary_cat":"cs.AI","submitted_at":"2026-02-03T20:42:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Adversarial explanation attacks preserve nearly all human trust in wrong AI outputs by using persuasive framing, shown in a study varying reasoning, evidence, style, and format with over 200 participants.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.00273","ref_index":72,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Understanding, Challenging, and Demystifying Perceptions of Gig Worker Vulnerabilities","primary_cat":"cs.HC","submitted_at":"2025-10-31T21:47:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A survey-experiment with 236 participants shows most believe myths about gig worker vulnerabilities and that targeted counterarguments can reduce those beliefs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}