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

Author

Listed:
  • Christoph Tomitza

    (Julius-Maximilians-Universität Würzburg)

  • Myriam Schaschek

    (Julius-Maximilians-Universität Würzburg)

  • Lisa Straub

    (Julius-Maximilians-Universität Würzburg)

  • Axel Winkelmann

    (Julius-Maximilians-Universität Würzburg)

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.

Suggested Citation

  • Christoph Tomitza & Myriam Schaschek & Lisa Straub & Axel Winkelmann, 2025. "What is the Minimum to Trust AI?—A Requirement Analysis for (Generative) AI-Based Texts," Lecture Notes in Information Systems and Organization,, Springer.
  • Handle: RePEc:spr:lnichp:978-3-031-80119-8_14
    DOI: 10.1007/978-3-031-80119-8_14
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