Explainable Artificial Intelligence (XAI) in Auditing

International Journal of Accounting Information Systems

58 Pages Posted: 14 Dec 2021 Last revised: 5 Aug 2022

See all articles by Chanyuan (Abigail) Zhang

Chanyuan (Abigail) Zhang

The University of Texas at San Antonio, Alvarez College of Business

Soohyun Cho

Rutgers, The State University of New Jersey

Miklos Vasarhelyi

Rutgers, The State University of New Jersey - Accounting & Information Systems

Date Written: Aug 1, 2022

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention regarding their potential applications in auditing. One major challenge of their adoption in auditing is the lack of explainability of their results. As AI/ML matures, so do techniques that can enhance the interpretability of AI, a.k.a., Explainable Artificial Intelligence (XAI). This paper introduces XAI techniques to auditing practitioners and researchers. We discuss how different XAI techniques can be used to meet the requirements of audit documentation and audit evidence standards. Furthermore, we demonstrate popular XAI techniques, especially Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), using an auditing task of assessing the risk of material misstatement. This paper contributes to accounting information systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of AI applications applied to auditing tasks.

Keywords: Explainable Artificial Intelligence (XAI), Auditing, Machine Learning, Material Restatement, LIME, SHAP

JEL Classification: M41, M42

Suggested Citation

Zhang, Chanyuan (Abigail) and Cho, Soohyun and Vasarhelyi, Miklos, Explainable Artificial Intelligence (XAI) in Auditing (Aug 1, 2022). International Journal of Accounting Information Systems, Available at SSRN: https://ssrn.com/abstract=3981918 or http://dx.doi.org/10.2139/ssrn.3981918

Chanyuan (Abigail) Zhang (Contact Author)

The University of Texas at San Antonio, Alvarez College of Business ( email )

United States

Soohyun Cho

Rutgers, The State University of New Jersey ( email )

One Washington Place, 906
Newark, NJ 07102
United States

Miklos Vasarhelyi

Rutgers, The State University of New Jersey - Accounting & Information Systems ( email )

96 New England Avenue, #18
Summit, NJ 07901-1825
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
817
Abstract Views
2,245
Rank
58,056
PlumX Metrics