User Stress in Artificial Intelligence: Modeling in Case of System Failure

Abstract

The uninterrupted operation of systems with artificial intelligence (AI) ensures high productivity and accuracy of the tasks performed. The physiological state of AI operators indicates a relation with an AI system failure event and can be measured through electrodermal activity. This study aims to model the stress levels of system operators based on system trustworthiness and physiological responses during a correct AI operation and its failure. Two groups of 18 and 19 participants took part in the experiments using two different types of software with elements of AI. The first group of participants used English proofreading software and the second group used drawing software as the AI tools. During the tasks, the electrodermal activities of the participants as a stress level indicator were measured. Based on the results obtained, the users' stress was determined and classified using logistic regression models with an accuracy of approximately 70%. The insights obtained can serve AI product developers in increasing the level of user trust and managing the anxiety and stress levels of AI operators.

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