Audit Data Analytics and Jurors' Assessment of Auditor Negligence: The Effects of Follow-up Procedures and the Lack of a Standard

Abstract

While auditors are increasingly using audit data analytics (ADA), research is limited on whether this new technology will affect auditor liability. This study examines the effects of how auditors follow up on the large number of exceptions often identified by ADA, and the lack of a standard on the use of ADA, affects jurors’ assessments of auditor negligence. Consistent with the algorithm aversion literature, we find that jurors assess higher negligence when auditors use Artificial Intelligence (AI) to identify a sample of exceptions for follow up testing than if human audit team members identify exceptions for follow up. Higher perceptions of causation and foreseeability in the AI condition mediate this effect. We also find an interaction such that negligence is highest when auditors use AI to select exceptions for follow up testing and there is no ADA standard. To our knowledge, ours is the first study to consider algorithm aversion in the juror-auditor setting and to apply the culpable control model to emerging audit technologies.