Jul 2026· Journal of Media Psychology· 0 citations· 39 references
TL;DR
It is suggested that cross-domain differences are smaller than often assumed once stakes are controlled, and that stakes matter more than domain for understanding perceived risk when people evaluate AIES advice.
Abstract
Abstract: As artificial intelligence expert systems (AIES) are increasingly used in domains such as medicine, psychotherapy, law, finance, and education, it remains important to understand what shapes their perceived trustworthiness relative to human experts. A central limitation of prior research is that findings are often difficult to generalize across domains because domain and stakes are rarely varied together within the same design. We address this issue in a preregistered online vignette experiment in which UK participants ( N = 898) evaluated advice from an AIES and a human expert under low- and high-stakes conditions in one of five domains. Across all domains and both stake levels, human experts were perceived as more trustworthy than AIES, whereas AIES were perceived as riskier. For trustworthiness, between-domain effects were significant but small on average, mainly affecting the size of the human–AIES difference, which was largest in medicine and psychotherapy and smallest in education. For perceived risk, stakes played a larger role than domain, with high-stakes situations increasing perceived risk across domains. Importantly, trustworthiness and perceived risk did not change in parallel across conditions: Domain differences were comparatively small for trustworthiness, whereas stakes played a larger role in perceived risk. Overall, the findings suggest that cross-domain differences are smaller than often assumed once stakes are controlled, and that stakes matter more than domain for understanding perceived risk when people evaluate AIES advice.
Artificial intelligence is reshaping decisions that affect people, institutions, and societies. Understanding how to design, deploy, and govern AI systems that can be trusted is now essential in many disciplines. This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts. Developed from an internationally applicable educational framework, the book is designed for teaching and learning in computer science, data science, law, policy, business, and related fields. It equips students and professionals with the concepts and judgment needed to engage critically and responsibly with AI in practice. Combining ethics, governance, and practical insight, the book explains key concepts including transparency, fairness, accountability, human oversight, and stakeholder participation. An interdisciplinary approach makes the material accessible to both technical and non-technical audiences, with realistic scenarios and reflection questions so readers connect principles to real-world AI applications.
Andrea Aler Tubella, Virginia Dignum, Marçal Mora-Cantallops et al.· 0 citations
It is argued that psychological competence should become a core consideration for model providers, deploying organizations, researchers, and regulators concerned with the real-world effects of human-facing AI systems.
M. Economides, Paul M. Sacher, Samuel Salzer et al.· 0 citations
The importance of trust in artificial intelligence (AI) continues to grow, as trust is widely regarded as a critical prerequisite for organizational AI adoption. In this context, intention to use AI can be understood as a consequence of the decision to trust AI and is therefore strongly influenced by trust. Moreover, trust is regarded as essential for understanding the impact of increasing interaction with AI systems on both individuals and society. Much of the discussion on trust in AI relies on frameworks derived from trust in automation, but these approaches remain largely theoretical and insufficiently validated. One important empirical contribution addressing this gap is the path model developed by Karg, Ritz and Asprion (2025), which examined trust in ChatGPT using a student sample. This model conceptualizes perceived trustworthiness through performance, process, and purpose. Together with a user’s propensity to trust, these factors are assumed to determine trust in AI. Karg, Ritz and Asprion (2025) demonstrated that perceived trustworthiness is significantly shaped by users’ inherent propensity to trust, in turn, influences the intention to use AI. The present study replicates this path model using a business sample to assess the robustness of the original findings and to advance theory building. An online survey was conducted among 97 employees of a major Swiss bank, employing identical items and methodologies as in the original study. The replication largely supports the original findings. However, in contrast to the original study, performance did not significantly predict trust in AI in the business sample. The findings further reinforce the argument that users’ dispositional characteristics may play a more decisive role in shaping perceived trustworthiness of and trust in AI systems.
Jona Karg, Janine Jäger, Petra Maria Asprion· AHFE International· 0 citations
An AI-enabled lifecycle of Creation, Transformation, Transmission, Evaluation, Evaluation, and Governance is proposed and an AI-eWOM fit perspective is developed and a TCCM-organized research agenda identifies priorities for future research.
A. Joyal· Journal of business and mana...· 0 citations
Methods of Artificial Intelligence (AI) enable the personalization of information for individual user experiences in many domains; however, they can also conflict with established design principles, e.g., due to uncertainties regarding the real world. Building trust and understanding can serve as an approach to create a more balanced relationship between humans and AI. Building upon a pilot study, an online survey was conducted to investigate 12 individual aspects related to the topics of explainability and controllability. The results indicate that both topics, despite their different and numerous facets, are generally perceived as important by respondents; simultaneously, however, a wide dispersion of opinions is frequently observed. This could be an indication that, alongside a fundamental consensus, individual perspectives, technical knowledge and understanding, context-specific factors, or personal experiences play a role in the perception of such systems.