Application of artificial intelligence: risk perception and trust in the work context with different impact levels and task types

• Published in: AI & society 2024-10, Vol.39 (5), p.2445-2456
• Main Authors: Klein, Uwe, Depping, Jana, Wohlfahrt, Laura, Fassbender, Pantaleon
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Automation; Big Data; Computer Science; Control; Decision making; Decisions; Engineering Economics; Experiments; Human performance; Hypotheses; Logistics; Management decisions; Marketing; Mechatronics; Methodology of the Social Sciences; New employees; Open Forum; Organization; Perception; Perceptions; Performance evaluation; Performing Arts; Risk perception; Robotics; Skills; Trust
• ISSN: 0951-5666; 1435-5655
• DOI: 10.1007/s00146-023-01699-w

Following the studies of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018), this empirical study uses two scenario-based online experiments. The sample consists of 221 subjects from Germany, differing in both age and gender. The original studies are not replicated one-to-one. New scenarios are constructed as realistically as possible and focused on everyday work situations. They are based on the AI acceptance model of Scheuer (Grundlagen intelligenter KI-Assistenten und deren vertrauensvolle Nutzung. Springer, Wiesbaden, 2020) and are extended by individual descriptive elements of AI systems in comparison to the original studies. The first online experiment examines decisions made by artificial intelligence with varying degrees of impact. In the high-impact scenario, applicants are automatically selected for a job and immediately received an employment contract. In the low-impact scenario, three applicants are automatically invited for another interview. In addition, the relationship between age and risk perception is investigated. The second online experiment tests subjects’ perceived trust in decisions made by artificial intelligence, either semi-automatically through the assistance of human experts or fully automatically in comparison. Two task types are distinguished. The task type that requires “human skills”—represented as a performance evaluation situation—and the task type that requires “mechanical skills”—represented as a work distribution situation. In addition, the extent of negative emotions in automated decisions is investigated. The results are related to the findings of Araujo et al. (AI Soc 35:611–623, 2020) and Lee (Big Data Soc 5:1–16, 2018). Implications for further research activities and practical relevance are discussed.