Challenges for Leveraging Explainable Artificial Intelligence in Audit Procedures

Authors

Hanchi Gu
Huijue Kelly Duan, Sacred Heart UniversityFollow
Miklos A. Vasarhelyi, Rutgers University

Document Type

Peer-Reviewed Article

Publication Date

2025

Abstract

This paper discusses the challenges encountered by the audit industry in light of the dearth of well-labeled data and the increasing adoption of machine learning technologies. Although existing machine learning techniques have their merits, they have limitations when it comes to transactional data. Explainable artificial intelligence (XAI) can be a potential solution for applying machine learning models in audit procedures. Primarily, this study discusses challenges related to dependence on preprocessing, verification of explanation, variation in XAI techniques, limitations for feature importance explanation, auditors’ attitude to XAI, and computation time. The paper provides some potential solutions for these challenges.

Comments

JEL Classifications: M41; M42.

DOI

10.2308/JETA-2023-044

Recommended Citation

Gu, H., Duan, H. K., & Vasarhelyi, M. A. (2025). Challenges for leveraging Explainable artificial intelligence in audit procedures. Journal of Emerging Technologies in Accounting, 1-12. Doi: 10.2308/JETA-2023-044

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.