COMPLEX ESTIMATES AND AUDITOR RELIANCE ON ARTIFICIAL INTELLIGENCE

dc.contributor.author Joe, Jennifer
dc.contributor.author Commerford, Benjamin
dc.contributor.author Dennis, Sean
dc.contributor.author Wang, Jenny
dc.date.accessioned 2019-12-06T18:28:39Z
dc.date.available 2019-12-06T18:28:39Z
dc.date.issued 2019-08-07
dc.description.abstract Audit firms are investing millions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Audit firms assume AI will enhance audit quality. However, a growing body of research documents “algorithm aversion” – the tendency for individuals to discount computer-based advice more heavily than human advice, although the advice is identical otherwise. Auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users. Accordingly, we examine how algorithm aversion manifests in auditor decisions using an experiment that manipulates the source of contradictory audit evidence (human specialist versus AI specialist system) and the degree of structure within the client’s estimation process (higher versus lower) for a complex estimate. Consistent with theory, we find evidence that algorithm aversion amplifies the persuasive effect of greater estimation structure, making auditors more likely to discount contradictory audit evidence and accept management’s preferred estimates.
dc.identifier.uri http://hdl.handle.net/10125/64803
dc.subject artificial intelligence
dc.subject Data analytics
dc.subject complex estimates
dc.subject management bias
dc.subject audit technology
dc.subject algorithm aversion
dc.title COMPLEX ESTIMATES AND AUDITOR RELIANCE ON ARTIFICIAL INTELLIGENCE
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
HARC_2020_paper_46.pdf
Size:
556.81 KB
Format:
Adobe Portable Document Format
Description: