Credibility Assessment of Human–Generative AI Interaction

doi:10.1017/9781009587877.005

4 - Credibility Assessment of Human–Generative AI Interaction

Published online by Cambridge University Press: 19 September 2025

Yuxiang Chris Zhao ,

Shijie Song and

Yutian Jing

Edited by

Dan Wu and

Shaobo Liang

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Dan Wu: Affiliation:
Wuhan University, China
Shaobo Liang: Affiliation:
Wuhan University, China

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Summary

This chapter aims to provide a comprehensive overview of the current state of credibility research in human–generative AI interactions by analyzing literature from various disciplines. It begins by exploring the key dimensions of credibility assessment and provides an overview of two main measurement methods: user-oriented and technology-oriented. The chapter then examines the factors that influence human perceptions of AI-generated content (AIGC), including attributes related to data, systems, algorithms, and user-specific factors. Additionally, it investigates the challenges and ethical considerations involved in assessing credibility in human–generative AI interactions, scrutinizing the potential consequences of misplaced trust in AIGC. These risks include concerns over security, privacy, power dynamics, responsibility, cognitive biases, and the erosion of human autonomy. Emerging approaches and technological solutions aimed at improving credibility assessment in AI systems are also discussed, alongside a focus on domains where AI credibility assessments are critical. Finally, the chapter proposes several directions for future research on AIGC credibility assessments.

Keywords

Credibility Assessment Artificial Intelligence Human–Generative AI Interaction

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Type: Chapter
Information: Human-AI Interaction and Collaboration , pp. 82 - 109

DOI: https://doi.org/10.1017/9781009587877.005 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2025

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