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Project

The Impact of Perceived System Characteristics on Acceptance and Usage of AI Systems 

WorkgroupSocial Processes Lab 
Duration11/2020-11/2024
FundingSondertatbestand Data Science
Project description

Humans often tend to treat technical systems as social actors and ascribe them human-like characteristics (e.g., when telling a computer to work faster). With the ongoing introduction of Artificial Intelligence (AI), this tendency is likely to increase – as technical systems become more and more capable (e.g., of solving complex problems or adapting to individual users) and are often even explicitly designed to appear human-like.


Up to now, a multitude of research has focused on how the objective technical capabilities of different technologies impact users’ interaction behavior. However, especially users’ subjective perceptions of system characteristics – which can substantially differ from the objective features it provides – are important to understand users’ interaction with AI systems. To address this gap, this project aims to investigate how user’s subjective perceptions of human-like characteristics in AI systems influence the interaction with as well as the acceptance of such technical systems. Doing so, we seek to contribute to a better understanding of how users perceive such systems, as well as to grasp how this influences their subsequent responses.


The project is part of the „Human-Agent Interaction“ Network (MAI Network).

Cooperations

Prof. Dr. Kai Sassenberg, Leibniz-Institut für Psychologie (ZPID)

Publications

Gieselmann, M., & Sassenberg, K. (2023). The more competent, the better? The effects of perceived competencies on disclosure towards conversational Artificial Intelligence. Social Science Computer Review, 41(6), 2342-2363. https://dx.doi.org/10.1177/08944393221142787 [Data] Open Access
 

Tschopp, M., Gieselmann, M., & Sassenberg, K. (2023). Servant by default? How humans perceive their relationship with conversational AI. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 17(3), Article 9. https://dx.doi.org/10.5817/cp2023-3-9 Open Access
 

contact

Miriam Gieselmann Miriam Gieselmann
Tel.: +49 7071 979-204

Project team

Marisa Tschopp