arbiter
JournalsAccount

Artificial Intelligence in Auditing: How Auditor AI Use Can Mitigate Legal Liability

Robert Libby & Patrick D. Witz

Current Issues in Auditing2025https://doi.org/10.2308/ciia-2024-029article
AJG 2ABDC B
Weight
0.46

Abstract

SUMMARY Audit firms are investing billions of dollars into artificial intelligence (AI) technology. These investments have the potential to transform the audit landscape and potentially improve the efficiency and effectiveness of audit processes. These investments might also result in wider benefits for audit firms, as audit firms can leverage this technology to reinforce perceived objectivity and trust in the audit. This article summarizes a recent study by Libby and Witz (2024) that finds that the use of AI can help mitigate auditor liability by limiting the effects of the appearance of auditor independence conflicts on juror negligence assessments. This article discusses the implications of these findings. It discusses how these findings indicate a potential solution to a long-standing dilemma that audit firms have faced, where audit firms may desire to invest in favorable relationships with their audit clients while also being seen as performing objective and unbiased audit work.

4 citations

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.2308/ciia-2024-029

Or copy a formatted citation

@article{robert2025,
  title        = {{Artificial Intelligence in Auditing: How Auditor AI Use Can Mitigate Legal Liability}},
  author       = {Robert Libby & Patrick D. Witz},
  journal      = {Current Issues in Auditing},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/ciia-2024-029},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Artificial Intelligence in Auditing: How Auditor AI Use Can Mitigate Legal Liability

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.