The paper proposes a mental model-based framework of trust that captures multidimensional aspects of trust and can be used to infer human trust in AI agents. It formalizes trust perception, trust evolution, human reliance, and decision-making, and defines the appropriate level of trust in the agent. Human subject studies evaluate whether adjusting human beliefs about the agent, as predicted by the framework, can change trust perceptions across performance, process, and purpose dimensions.
By Zahra Zahedi, Sarath Sreedharan, Erin Chiou, Subbarao Kambhampati
arXiv:2608. 12372v1 Announce Type: new Abstract: AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers.
By Vijay Keswani, Breanna K. Nguyen, Cyrus Cousins, Vincent Conitzer, Walter Sinnott-Armstrong, Jana Schaich Borg
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
arXiv:2606. 12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions.
By Shayan Kiyani, Sima Noorani, George Pappas, Hamed Hassani
arXiv:2504.07170v3 Announce Type: replace
Abstract: Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainabi...
By Jesse C. Cresswell
arXiv:2607. 03025v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) across diverse areas of human activity-ranging from everyday tasks to safety-critical applications-aims to enhance decision-making effectiveness with minimal human feedback.
By Andreas Kouridakis, Dimitrios Patiniotis Spyropoulos, George Vouros
arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.
By Valerie Chen, Cleotilde Gonzalez, Anita Williams Woolley, Michael Lee, Tongshuang Wu, Vincent Conitzer, Aarti Singh
arXiv:2606. 06081v1 Announce Type: new Abstract: Appropriate reliance on AI advice has become a central research theme in human-AI collaboration.
By Ranjan Mishra, Jakob Schoeffer
arXiv:2505. 23397v3 Announce Type: replace Abstract: This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop decision making.
By Ahmad Mohsin, Helge Janicke, Ahmed Ibrahim, Iqbal H. Sarker, Seyit Camtepe
The paper introduces CARE, a calibrated adaptive rectification and escalation framework that integrates AI models with human reviewers to ensure safe, human-aligned decisions. CARE continuously learns from human feedback, improving automation while reducing the need for human queries. Experiments on driving, language, and robotics datasets show a 25–81% reduction in human queries compared to baseline methods.
By Chenyu Zhang, Rachel Luo, Boyi Li, Anjali Parashar, Marco Pavone, Apoorva Sharma
The paper titled "Whose Ground Truth? Embracing Ambiguity in Human-Centered AI" argues that current AI systems assume a single definitive ground truth, which ignores the inherent ambiguity in human interpretations. It calls for modeling the full space of plausible human judgments, distinguishing true ambiguity from noise, and suggests that this shift should influence how AI is represented, learned, evaluated, deployed, and governed.
By Jingyao Wu, Mohammad Tariqul Islam, Per R{\aa}dberg Nagb{\o}l, Julie Gerlings, Giovanni Leoni, Georgiana Fryza, Lars Pilegaard Thomsen, Jakob Mainz
The article discusses autonomous systems as the pinnacle of AI development, emphasizing the need to blend connectionist and symbolic AI within systems engineering. It introduces a generic agent architecture that organizes behavior around long‑term memory and outlines challenges in linking sensory data to structured memory, goal‑oriented decision making, planning, and agent coordination for collective intelligence. The authors also explore agent trustworthiness, noting it extends beyond behavior to include cognitive validity, and propose methods for its evaluation while highlighting the gap between current capabilities and the envisioned autonomous multi‑agent systems.
By Joseph Sifakis