题目:Man + machines: assessing the informativeness of critical audit matters identified by AI and human auditors
时间：2024年12月26日（周四）10:00-12:00
地点：浙江大学紫金港校区管理学院A423
主讲人：郭丰，爱荷华州立大学副教授
主持人：董望，浙江大学管理学院副教授
主讲人简介：
郭丰，爱荷华州立大学副教授。主要研究领域包括公司治理、人工智能、并购及审计，其研究成果发表于多家UTD24/FT50国际顶级期刊，如 The Journal of Finance, The Review of Financial Studies, The Journal of Financial Economics， The Journal of Financial and Quantitative Analysis, Contemporary Accounting Research 和 Information Systems Research 等。现担任 Managerial Auditing Journal 副主编，并担任多家国际期刊（包括 The Accounting Review, Contemporary Accounting Research, Management Science, Journal of Financial and Quantitative Analysis 及 Auditing: A Journal of Practice and Theory）的匿名审稿人。他于2010年获得南京大学商学院学士学位，2013年获得明尼苏达大学会计与应用经济硕士学位，2018年获得堪萨斯大学会计博士学位。
摘要：
We explore the potential value of generative artificial intelligence (AI) in identifying critical audit matter (CAM) topics and assess the informativeness of CAMs identified by AI and human auditors. We compare AI-generated CAMs with those reported by human auditors to evaluate their effectiveness in signaling financial reporting risks to investors. We further discern the overlapping and non-overlapping information between AI-generated CAMs and auditor-reported CAMs to investigate the (dis)advantages of AI relative to human auditors. While AI has superior information-processing capabilities and is less affected by human biases, it may be less informative than human auditors due to the lack of professional judgment and access to private client information. Our findings indicate that AI can predict approximately 37% of CAMs reported by auditors. Auditor-reported CAMs or AI-generated CAMs alone are generally not significantly associated with financial reporting risk or abnormal stock returns. However, the overlapping CAMs identified by auditors and generated by AI are significantly associated with financial reporting risk and abnormal stock returns, whereas CAMs identified exclusively by auditors or AI are not, suggesting a complementary role of generative AI and human auditors in identifying CAMs. Overall, our findings indicate that combining AI and auditor reports can help investors identify significant financial reporting risks and also highlight the information value of CAMs reported by human auditors.
