The Public Company Accounting Oversight Board recently proposed amendments to the standard audit report that would require the disclosure of critical audit matters (CAMs), and the Securities and Exchange Commission continues to evaluate the use of principles-based (imprecise) accounting standards within U.S. generally accepted accounting principles. We assert that jurors perceive precise accounting standards to constrain auditors' control over financial reporting outcomes, resulting in a lower propensity for negligence verdicts when the accounting treatment conforms to the precise standard. However, we hypothesize that the use of either imprecise standards or CAMs reduces the extent to which jurors perceive this constraint to exist, leading to increased auditor liability. We present experimental evidence supporting this argument. Our results highlight the similarities between the effects of imprecise accounting standards and CAMs on negligence assessments. These results provide insight for regulators and the auditing profession about the potential consequences of the proposed regulatory changes.

SUMMARY The U.S. Public Company Accounting Oversight Board recently proposed changes to the audit reporting model that would require auditors to disclose areas of high audit risk within the audit report. Concerns about the proposal's potential to increase auditor liability have been raised by practitioners and highlighted in the business press. In this paper, we review five recent experiments that directly relate to these concerns, identify patterns in the results, and discuss the implications of these findings for regulators and practitioners.

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research.

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