Regulators now require auditors to provide information about how they evaluate complex estimates. Because users encounter this auditor-provided information alongside management-provided information, we jointly examine the value relevance of these disclosures. We also examine whether visual cues in audit reports influence how nonprofessional investors use these disclosures. We find that disclosures from managers and auditors provide different value-relevant information about the same underlying issue. While users struggle to weight fully narrative auditor disclosures in their valuation judgments without corresponding management disclosures, visual cues facilitate their weighting of information about the audit. Specifically, users take increased price protection when auditor disclosures also include visual cues. However, consistent with market signaling theory, corresponding voluntary disclosures from management attenuate this price protection. This suggests management can mitigate negative valuation effects that may arise from auditor disclosures, and implies that visual cues in audit reports can prompt managers to increase disclosure transparency. Data Availability: Contact the authors.

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.