We provide evidence that regulatory guidance aimed at improving audit efficiency and effectiveness—allowing auditor reliance on a multi‐location client's competent and objective internal audit function (IAF)—can unintentionally increase auditors' litigation risk. Our research is important in demonstrating how client characteristics and juror cognitive processing, such as the number of client locations and jurors' susceptibility to the numerosity heuristic, factors beyond auditors' control, can exacerbate their litigation exposure. Consistent with theoretical predictions, we find that susceptibility to the numerosity heuristic contributes to jurors assessing an increased likelihood of misstatement on multi‐location compared to single‐location audits. Furthermore, these assessments of higher misstatement risk on multi‐location audits lead jurors to perceive that auditor reliance on the client's IAF in multi‐location audits is less appropriate (i.e., not normal). Accordingly, jurors judge that auditors are more negligent when they rely on the IAF during multi‐location audits than when they do not, but IAF reliance does not impact auditor negligence on single‐location audits. Our results suggest auditor reluctance to use a qualified IAF, despite client pressure and regulatory allowance, can provide potential benefits to firms in terms of reduced litigation exposure. Thus, we demonstrate the legal regime can undermine the objectives of regulators' guidance to enhance audit efficiency and corporate governance.

Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion”—the tendency to discount computer‐based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm's AI system (instead of a human specialist) propose smaller adjustments to management's complex estimates, particularly when management develops their estimates using relatively objective (vs. subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.