TL;DR
This conceptual and normative paper links together research on anthropomorphism, mental models, trust calibration and AI-assisted decision-making into a single end-to-end chain, proposing a conceptual model with propositions for empirical testing.
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, designed for teaching and learning in computer science, data science, law, policy, business, and related fields.
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.
The present study replicates the path model developed by Karg, Ritz and Asprion (2025) using a business sample to assess the robustness of the original findings and to advance theory building, which largely supports the original findings.
The paper concludes that responsible AI adoption should be treated as a social trust calibration problem rather than a simple technology acceptance problem, and offers practical guidance for leaders, educators and policymakers seeking to scale AI while preserving human judgement, legitimacy and social confidence.
It is argued that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.
The research adds to current literature, combining XAI characteristics with value-based and technology acceptance models, thus providing a more in-depth insight into the AI adoption in the new markets.
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