What is the Minimum to Trust AI?—A Requirement Analysis for (Generative) AI-Based Texts

Christoph Tomitza (), Myriam Schaschek (), Lisa Straub () and Axel Winkelmann ()
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Christoph Tomitza: Julius-Maximilians-Universität Würzburg
Myriam Schaschek: Julius-Maximilians-Universität Würzburg
Lisa Straub: Julius-Maximilians-Universität Würzburg
Axel Winkelmann: Julius-Maximilians-Universität Würzburg

A chapter in Conceptualizing Digital Responsibility for the Information Age, 2025, pp 213-229 from Springer

Abstract: Abstract The generative Artificial Intelligence (genAI) innovation enables new potentials for end-users, affecting youth and the inexperienced. Nevertheless, as an innovative technology, genAI risks generating misinformation that is not recognizable as such. The extraordinary AI outputs can result in increased trustworthiness. An end-user assessment system is necessary to expose the unfounded reliance on erroneous responses. This paper identifies requirements for an assessment system to prevent end-users from overestimating trust in generated texts. Thus, we conducted requirements engineering based on a literature review and two international surveys. The results confirmed the requirements which enable human protection, human support, and content veracity in dealing with genAI. Overestimated trust is rooted in miscalibration; clarity about genAI and its provider is essential to solving this phenomenon, and there is a demand for human verifications. Consequently, our findings provide evidence for the significance of future IS research on human-centered genAI trust solutions.

Keywords: Human-centered; Generative AI; Trust; Requirements; ChatGPT (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-80119-8_14

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