Trust in AI

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14 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Latest papers 103

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Capability Advertisement as a Market for Lemons: A Trust Layer for Heterogeneous Agent Networks
Jun 2, 2026Gaurav Naresh MittalAI Agent Reliability Agent Communication Protocols
Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
May 29, 2026Mert Yazan, Suzan Verberne, Frederik Bungaran Ishak SitumeangTrust in AI Conversational Agents
Amsterdam University of Applied Sciences Amsterdam, Netherlands · University of Leiden Leiden, Netherlands
Dissociative Identity: Language Model Agents Lack Grounding for Reputation Mechanisms
May 28, 2026Botao Amber Hu, Helena Rong, Max Van KleekAI Agent Monitoring AI Agent Governance
University of Oxford, UK · New York University Shanghai, China
The Decision to Verify: How Warmth and User Characteristics Shape Reliance on Conversational Agents for Information Search
May 27, 2026Mert Yazan, Frederik Bungaran Ishak Situmeang, Suzan VerberneConversational Search Trust in AI
AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?
May 27, 2026Maharshi Gor, Yoo Yeon Sung, Yu Hou +4Human-AI Agreement Trust in AI
University of Maryland College Park, MD, USA · University of California Berkeley, CA, USA · Phasechange.ai Lakewood, CO, USA +1
I Hear, Therefore I Trust: A Socio-Technical Investigation of Humans as Synthetic Speech Detectors
May 27, 2026Lelia Erscoi, Tomi KinnunenAudio Deepfake Detection Trust in AI
Computational Speech Group, University of Eastern Finland · Computational Speech Group, University of Eastern Finland, Joensuu, Finland
The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams
May 25, 2026Christopher Baker, Stephen Hinton, Akashdeep Nijjar +4Brain-Computer Interfaces Trust in AI
School of Electronics, Electrical Engineering and Computer Science Queen's University Belfast · School of Psychology Liverpool John Moores University · School of Computer Science and Electronic Engineering University of Essex +1
Eroding Trust in Real Speech: A Large-Scale Study of Human Audio Deepfake Perception
May 21, 2026Nicolas M. Müller, Wei Herng ChoongAudio Deepfake Detection Speech Processing
Fraunhofer AISEC & Resemble AI · Germany · Fraunhofer AISEC
The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems
May 21, 2026Dongxin GuoTransformer Expressivity Trust in AI
UNIVERSITY OF HONG KONG
Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: An Experimental Investigation
May 21, 2026Arif Ahmed, Gondy Leroy, Agrim Sachdeva +4Trust in AI
The University of Arizona, Tucson 85721, U.S.A
Adversarial Trust Poisoning in Vehicular Collaborative Perception
May 21, 2026Yutong Liu, Chenyi Wang, Ming F. Li +1Autonomous Driving Trust in AI
ECE Department, The University of Arizona, Tucson, AZ
Understanding Perspectives of Patients, Caregivers and Clinicians towards Emerging Collaborative-decision Making Technologies
May 20, 2026Ray-Yuan Chung, Athena Ortega, Zixuan Xu +7Clinical Decision Support Clinical Decision-Making
University of Washington, Seattle, WA · Johns Hopkins University, Baltimore, MD
Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs
May 19, 2026Lucie Galland, Chloé Clavel, Magalie OchsGender Bias in Language Models Trust in AI
LIS Laboratory, Amu · Marseille, France · Inria Paris +1
AI Technologies in Language Access: Attitudes Towards AI and the Human Value of Language Access Managers
May 19, 2026Miguel A. Jiménez-Crespo, Stephanie Rodriguez, Alejandro Jaume LosaTrust in AI Human Oversight of AI
Rutgers University/ Dept. of Spanish and Portuguese, 15 Seminary Pl 5th fl., New Brunswick, NJ, 07030, USA
Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
May 18, 2026Changkun OuTool-Using Agents Safe Bayesian Optimization
Generative AI Advertising as a Problem of Trustworthy Commercial Intervention
May 18, 2026Jingyi Qiu, Qiaozhu MeiAI Accountability Online Advertising
School of Information University of Michigan
Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
May 18, 2026Alfio Ventura, Tim Katzke, Jan Corazza +1Trust in AI Explainability Evaluation
Research Center Trustworthy Data Science and Security, University Alliance Ruhr, Joseph-von-Fraunhofer-Str. 25, Dortmund, 44227, Germany · University of Duisburg-Essen, Bismarckstraße 120, Duisburg, 47057, Germany · TU Dortmund University, August-Schmidt-Straße 1, Dortmund, 44227, Germany
To Trust or Not to Trust: Authors' Response to AI-based Reviews
May 15, 2026César Leblanc, Lukas PicekScholarly Peer Review Trust in AI
École Normale Supérieure, Paris, France. · Sorbonne University, Paris, France. · University of West Bohemia, Plzeň, Czech Republic. +1
Voice "Cloning" is Style Transfer
May 15, 2026Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds +3Controllable Speech Generation Trust in AI
Cornell University · TogetherAI · Stanford University
The End of Trust: How Agentic AI Breaks Security Assumptions
May 14, 2026Osama Zafar, Alexander Nemecek, Erman AydayAI Agent Security AI Agent Governance
Dept. of Computer and Data Sciences Case Western Reserve University Cleveland, USA
Beyond Anthropomorphism: Exploring the Roles of Perceived Non-humanity and Structural Similarity in Deep Self-Disclosure Toward Generative AI
May 13, 2026Satoru ShibuyaHuman-AI Interaction Human-Centered AI
Graduate School of Business and Finance, Waseda University
Embodied AI in Action: Insights from SAE World Congress 2026 on Safety, Trust, Robotics, and Real-World Deployment
May 11, 2026Jan-Mou Li, Paul Schmitt, Wei Tong +4AI Governance Trust in AI
Machine Psychometrics: A Mathematical Psychology of Artificial Intelligence
May 10, 2026Alex Bogdan, Adrian de Valois-FranklinAI Agent Evaluation AI Agent Monitoring
Evolutionairy AI · Toronto, Canada
Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration
May 8, 2026Junjie Xu, Xingjiao Wu, Zihao Zhang +6Human-AI Interaction AI Agent Governance
East China Normal University, Shanghai, China. · National University of Singapore, Singapore.
Trust Without Trusting: A Recomputable Trust Protocol for Autonomous Agents
May 7, 2026Lars Kersten KroehlAI Agent Governance Trust in AI
MolTrust / CryptoKRI GmbH, Zurich
Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
May 5, 2026Lisa C. Adams, Linus Marx, Erik Thiele Orberg +8Healthcare Clinical Decision Support
Department of Diagnostic and Interventional Radiology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Radiation Oncology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Internal Medicine III: Hematology and Clinical Oncology, University Hospital Regensburg, University of Regensburg, Regensburg, Germany +4
Less Interaction But More Explanation: A Communication Perspective on Agentic AI Interfaces
May 2, 2026Eunchae Jang, S. Shyam SundarExplainable Artificial Intelligence Human-AI Interaction
Media Effects Research Laboratory, Pennsylvania State University, Pennsylvania, USA · Center for Socially Responsible AI, Pennsylvania State University, Pennsylvania, USA
Hallucinations Undermine Trust; Metacognition is a Way Forward
May 2, 2026Gal Yona, Mor Geva, Yossi MatiasLLM Hallucination Mitigation Hallucination in Language Models
Google Research · Tel Aviv University
"I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation
May 1, 2026Daan Di Scala, Maaike de Boer, Pınar YolumRetrieval-Augmented Generation LLM Reliability
TNO Netherlands Organisation for Applied Scientific Research, Department Data Science · Utrecht University, Department of Information and Computing Sciences
Consumer Attitudes Towards AI in Digital Health: A Mixed-Methods Survey in Australia
Apr 30, 2026Wei Zhou, Rashina Hoda, Joycelyn LingHealthcare Human-Centered AI
1*Faculty of Information Technology, Monash University, Wellington Rd, Clayton, 3800, VIC, Australia. · 2Digital Health CRC, George Street, Sydney, 2000, NSW, Australia.

Jun 2, 2026, cs.MAJ/K move · Enter open · S save

Capability Advertisement as a Market for Lemons: A Trust Layer for Heterogeneous Agent Networks

Gaurav Naresh Mittal

AI Agent Reliability Agent Communication Protocols Trust in AI Multi-Agent Communication LLM Agent Reliability

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Large language model (LLM) agents have begun to delegate work to one another. Protocols such as the Model Context Protocol (MCP) and the Agent2Agent protocol (A2A) let an agent publish what it can do and let others call it, and public registries of such agents are already appearing. These protocols assume an advertised capability is a static, truthful fact. A real agent is none of these things: its competence is probabilistic, varies with input, drifts when the underlying model is updated, and, because the agent is itself a language model, it can describe itself with complete confidence and be wrong. A caller therefore sees what an agent claims to do, not what it can do, with no principled way to tell a reliable provider from a fluent impostor. We argue these difficulties share one cause: the market for lemons. When quality is hidden and claims are cheap, good and bad providers become indistinguishable, honest reliability goes unrewarded, and the market decays toward its worst participants. Economics offers three remedies, signaling, screening, and reputation, and none are present in today's agent protocols. We make four contributions: (1) a failure taxonomy that names confident-wrong as a non-adversarial, correlated subclass of Byzantine faults that classical fault-tolerance mismodels; (2) a market-for-lemons model showing that faith-based protocols admit only a low-trust equilibrium; (3) the Trust Layer, a thin, protocol-agnostic narrow waist above MCP and A2A that adds probabilistic capability descriptors, screening, and reputation, and admits a separating equilibrium when the cost of sustaining an overclaim exceeds the gain from it; and (4) a reliability-composition bound for delegation chains with an end-to-end placement argument. The design needs no model retraining and degrades gracefully when its trust anchors are absent or corrupt.

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