The Impact of Direct and Third-Party Control: A Comparison of the Usage of AI Advice in Hiring Decisions

Abstract

For the trustworthiness of AI-based systems and their usage, control plays an important role from both regulatory and end-user perspectives. In general, two control approaches can be distinguished: direct control, giving the end user greater influence over the AI system, or a more indirect approach, by involving third parties to exercise control over the system. In this between-subjects experiment with 181 participants, four conditions with direct and indirect (third-party) control measures were compared in their usage of the AI systems' recommendations. During this study, participants evaluated the fit of fictional applications for a job opening. To assess system usage, we used the weight of advice (WOA), measuring the extent to which recommendations were considered in participants' assessments. A one-way ANOVA found a significant difference in the WOA between the levels of control: F(3, 183) = 2.81, p = .041. Group comparisons via contrasts showed a significant difference between the comparator and the third-party verification group (0.27; SE = 0.10; p = .011). Descriptively, all three experimental groups showed a higher usage (WOA) than the comparator group. This study shows the potential for control measures to deliver more trustworthy AI systems that see a higher usage of their recommendations. Thus, it provides practical implications for future design of AI-based decision support systems.

Keywords: Human-AI-Interaction, Human-AI-Collaboration, Trustworthy AI, Control

DOI: 10.54941/ahfe1007506