Control issues: How providing input affects auditors' reliance on artificial intelligence

Abstract

In this study, we examine auditors' reliance on artificial intelligence (AI) systems that are designed to provide evidence around complex estimates. In an experiment with highly experienced auditors, we find that auditors are more hesitant to rely on evidence from AI-based systems compared to human specialists, consistent with algorithm aversion. Importantly, we also find that a small amount of control (i.e., providing input to specialists) can mitigate this aversion, though this effect depends on auditors' personal locus of control (LOC). Providing input increases reliance on evidence from AI systems for auditors who believe they have little control over their outcomes (i.e., an external LOC). In contrast, auditors with an internal LOC are particularly hesitant to rely on AI-based evidence, and providing input has little impact on their reliance. Interviews with experienced auditors corroborate our findings and suggest auditors feel a greater sense of control working with human specialists relative to AI-based systems. Overall, our results suggest perceived control plays an important role in auditors' aversion to AI and that auditors' individual traits can affect this aversion.