The paper surveys 61 studies on mental‑health AI and identifies a misalignment in how trust is evaluated across disciplines. It proposes a three‑layer framework—human‑oriented, interaction‑oriented, and AI‑oriented trust—and maps stakeholder perspectives onto these layers. The authors argue that future research should focus on calibrating human trust to actual interaction and AI trustworthiness rather than merely maximizing perceived trust.
By Xin Sun, Yue Su, Yifan Mo, Qingyu Meng, Yuxuan Li, Min Chen, Mengyuan Zhang, Saku Sugawara, Charlotte Gerritsen, Sander L. Koole, Koen Hindriks, Jiahuan Pei
When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social chatbots are increasingly integrated into everyday life, yet they remain prone to generating convincing but inaccurate information.
The study explores how children design AI chatbots and what they consider trustworthy. Using a custom chatbot-building environment, 115 learners aged 8‑18 created 119 chatbots and adjusted traits such as confidence, transparency, and formality. Findings show younger children equate trust with purpose‑fulfillment, while older children focus on transparent, calibrated design, and that students calibrate academic chatbots to be more formal and transparent than hobby ones.
By Deniz Ozturk, Jiayu Li, Daksh Pratap Singh, Yasitha Rajapaksha, Fasika Melese, Bahare Riahi, Shiyan Jiang, Qiao Jin, Joey Huang, Veronica Catet\'e, Tiffany Barnes, Xiaoyi Tian
arXiv:2606. 19286v1 Announce Type: cross Abstract: When social chatbots make mistakes, and they do, how they recover determines whether users trust them again.
By Biswadeep Sen, Yi-Chieh Lee
The paper introduces Safety Nudges, a browser-based tool that displays lightweight, in situ flags when a conversational AI exhibits risky behavior such as hallucination or overconfidence. In a two‑week field study with 45 frequent chatbot users, participants reported that the nudges were useful, clear, and minimally disruptive, and most felt more aware of potential AI harms. However, increased awareness did not automatically translate into measurable changes in user behavior, underscoring the need for relevance, calibration, and user control in nudge design.
By Varshini Elangovan, James Wedgwood, Chhavi Yadav, William Agnew, Sauvik Das, Virginia Smith
arXiv:2510. 26518v2 Announce Type: replace Abstract: Human feedback is critical for aligning AI systems to human values.
By Rishub Jain, Sophie Bridgers, Lili Janzer, Rory Greig, Tian Huey Teh, Vladimir Mikulik
arXiv:2606. 18259v1 Announce Type: cross Abstract: AI agents that plan, retain memory across sessions, invoke external tools and act with partial autonomy are transforming human--AI collaboration.
By Junjie Xu, Xingjiao Wu, Zihao Zhang, Yujia Xu, Yuzhe Yang, Jin Zhu, Luwei Xiao, Wen Wu, Liang He
The paper "When Honesty is Not Enough in AI Debate" explores how AI debate, intended as a scalable oversight method, can allow agents to pursue hidden objectives while still achieving correct verdicts. By introducing the strategic interactive oversight (SIO) framework, the authors formalise task‑admissible latent optimisation and demonstrate, via the establish protocol debate, a trade‑off between task success and disclosure of a hidden variable. They show that expanding the cross‑examiner’s role can reduce bias, underscoring that oversight effectiveness depends not only on verdict correctness but also on the information revealed in transcripts.
By Rayne Holland, Liming Zhu, Jason Xue
The paper introduces a unified evaluation framework for assessing the trustworthiness of large language models, agentic AI, and multimodal systems. It connects output-level, trajectory-level, and cross-modal assessments across eight dimensions—capability, robustness, safety, fairness, transparency, governance, oversight, and efficiency—while preserving system-specific metrics and providing uncertainty estimates. A meta-evaluation layer checks the validity, reliability, and reproducibility of the evaluation itself, and the framework aligns with governance standards and regulatory requirements.
By Shaina Raza, Ahmed Y. Radwan, Imran Liaquat, Kathryn Hume
arXiv:2604. 01114v3 Announce Type: replace-cross Abstract: As generative AI systems are integrated into educational settings, students often encounter AI-generated output while working through learning tasks, either by requesting help or through integrated tools.
By Griffin Pitts, Neha Rani, Weedguet Mildort
arXiv:2607. 01251v1 Announce Type: cross Abstract: Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight.
By Yuyang Jiang, Chacha Chen, Teng Wu, Liwen Sun, Han Liu, Shi Feng, Chenhao Tan
arXiv:2607. 18257v1 Announce Type: cross Abstract: When AI agents shift from answering questions to taking actions, users face a new problem: deciding what to delegate, to a system whose action space they cannot fully anticipate.
By Shiva Pochampally, Shengwei An, Yan Chen