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

Olga Vl Bitkina,Jaehyun Park,Jungyoon Kim,Hyun K Kim,Jangwoon Park

doi:10.1109/access.2021.3117120

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 relationship 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 people participated 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 tool. 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.

Highlights

According to numerous official dictionaries, artificial intelligence (AI) is the capability of a machine to imitate intelligent human behavior [1]
EXPERIMENT 1: DRAWING SOFTWARE USING AI During the drawing AI software experiment, stress classification was performed in binary scale with low and high levels based on detected physiological response from the measured EDA signal
This study reported that 37% of men and 55% of women have anxiety about driverless car safety owing to the possibility of failure, and only 6% of people would put

Summary

Introduction

According to numerous official dictionaries, artificial intelligence (AI) is the capability of a machine to imitate intelligent human behavior [1]. Widespread adoption of AI can be attributed to the positive perception of novel technologies and innovations by users and customers; issues of user acceptance and trust in AI technology are becoming increasingly pressing every year [3]. Perceived technological characteristics of AI improve technology acceptance and use. These characteristics can improve the safety and performance of AI systems. Human actions and movement recognition can be used in smart homes and automated office AI environments to improve user comfort and safety [4], [5]. A prior study [4] elucidated this connection, based on AI environments, which could detect user actions to increase user comfort. The corresponding safety issues were analyzed and an automatic crime detection method for AI environments was proposed [5].

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