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Project

Psychological Determinants of Risk Perception about Artificial Intelligence

WorkgroupPerception and Action Lab
Duration10/2020-open
FundingIWM budget resources
Project description

In societal discourse, Artificial Intelligence (AI) is strongly tied with both opportunities and risks. In this project, it is investigated how humans perceive the risks of AI and how their risk assessments are associated with psychological factors like prior knowledge and judgmental confidence. The behavioral consequences of risk perception are investigated, as well as intervention methods aimed at raising an awareness of AI risks.


There is reason to believe that risk perception will be dependent on prior knowledge of a person. Laypeople have relatively low prior knowledge, and the associated uncertainty may make them more risk-averse, thus leading to a rejection of AI opportunities. In contrast, experts might be more inclined to believe that risks are controllable, and they might also be motivated to downplay risks of AI. The project examines such patterns – not only with regard to risk assessments, but also with regard to subsequent risk behavior (e.g., whether individuals will embrace or reject an AI-generated judgment).


In addition it will be tested how risks of AI could be made more tangible in interaction (e.g., through visualizations of data traffic) in order to impact risk perceptions. On the basis of these tests, interventions could be developed that prevent “risk-blindness”.

Cooperations
Publications

Said, N., Potinteu, A. E., Brich, I., Buder, J., Schumm, H., & Huff, M. (2023). An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception. Computers in Human Behavior, 149, Article 107855. https://dx.doi.org/10.1016/j.chb.2023.107855 request document
 

Schwesig, R., Brich, I. R., Buder, J., Huff, M., & Said, N. (2023). Using Artificial Intelligence (AI)? Risk and opportunity perception of AI predict people’s willingness to use AI. Journal of Risk Research, 26(10), 1053-1084. https://dx.doi.org/10.1080/13669877.2023.2249927 request document