{"title":"Exploring the Impact of Technology Dominance on Audit Professionalism through Data Analytic-Driven Healthcare Audits","authors":"Jared Koreff, Lisa Baudot, Steve G. Sutton","doi":"10.2308/isys-2022-023","DOIUrl":null,"url":null,"abstract":"ABSTRACT Artificial intelligence (AI)-enabled tools and analytics hold the potential to radically alter audit processes by disseminating centralized audit expertise. We examine this potential in the context of data analytic-driven audits mandated to reduce fraud, waste, and abuse in a government-sponsored healthcare program. To do so, we draw on semistructured interviews with healthcare providers (i.e., auditees) subject to healthcare audits. Our work shows how use of paraprofessional auditors guided by AI-enabled tools and analytics reflects a very different audit environment. Specifically, auditees’ experiences suggest paraprofessional auditors lack specific expertise and credentials to conduct data-driven audits, apply judgment in deference to technology, and disregard the impact of AI-driven decisions on the public interest. Such experiences raise potential concerns for all audits over unbridled use of AI-enabled tools and analytics by novice-level auditors/paraprofessionals, but even more for audits conducted in contexts where adherence to professional norms is essential to minimizing public interest consequences. JEL Classifications: M42; M48.","PeriodicalId":46998,"journal":{"name":"Journal of Information Systems","volume":"59 1","pages":"0"},"PeriodicalIF":2.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2308/isys-2022-023","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
ABSTRACT Artificial intelligence (AI)-enabled tools and analytics hold the potential to radically alter audit processes by disseminating centralized audit expertise. We examine this potential in the context of data analytic-driven audits mandated to reduce fraud, waste, and abuse in a government-sponsored healthcare program. To do so, we draw on semistructured interviews with healthcare providers (i.e., auditees) subject to healthcare audits. Our work shows how use of paraprofessional auditors guided by AI-enabled tools and analytics reflects a very different audit environment. Specifically, auditees’ experiences suggest paraprofessional auditors lack specific expertise and credentials to conduct data-driven audits, apply judgment in deference to technology, and disregard the impact of AI-driven decisions on the public interest. Such experiences raise potential concerns for all audits over unbridled use of AI-enabled tools and analytics by novice-level auditors/paraprofessionals, but even more for audits conducted in contexts where adherence to professional norms is essential to minimizing public interest consequences. JEL Classifications: M42; M48.
期刊介绍:
The Journal of Information Systems (JIS) is the academic journal of the Accounting Information Systems (AIS) Section of the American Accounting Association. Its goal is to support, promote, and advance Accounting Information Systems knowledge. The primary criterion for publication in JIS is contribution to the accounting information systems (AIS), accounting and auditing domains by the application or understanding of information technology theory and practice. AIS research draws upon and is informed by research and practice in management information systems, computer science, accounting, auditing as well as cognate disciplines including philosophy, psychology, and management science. JIS welcomes research that employs a wide variety of research methods including qualitative, field study, case study, behavioral, experimental, archival, analytical and markets-based.