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.

人工智能 (AI) 和机器学习 (ML) 在审计中的潜在应用越来越受到关注。在审计中采用它们的一个主要挑战是它们的结果缺乏可解释性。随着 AI/ML 的成熟，可以增强 AI 可解释性的技术也越来越成熟，也就是可解释人工智能 (XAI)。本文向审计从业者和研究人员介绍了 XAI 技术。我们讨论了如何使用不同的 XAI 技术来满足审计文件和审计证据标准的要求。此外，我们使用评估重大错报风险的审计任务展示了流行的 XAI 技术，尤其是本地可解释模型无关解释 (LIME) 和 Shapley 附加解释 (SHAP)。