{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T02:59:22Z","timestamp":1780369162518,"version":"3.54.1"},"reference-count":61,"publisher":"American Accounting Association","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>As environmental, social, and governance (ESG) reporting has become a mainstream channel for companies to communicate their commitment to sustainability issues, the need for reliable and transparent ESG reports is increasing. However, research on ESG assurance is still in its early stages. ESG assurance poses more challenges than traditional financial auditing due to the diverse subjects and types of information in ESG reports. This paper proposes using artificial intelligence (AI) technologies and exogenous data as solutions. It discusses how AI can enhance the efficiency and effectiveness of ESG assurance by assessing vast and extensive data. This paper also explores AI\u2019s application throughout the general ESG assurance process and contributes to the discussion on providing high-quality ESG assurance services. Additionally, it provides practical implications for auditors, regulators, and stakeholders.<\/jats:p>","DOI":"10.2308\/jeta-2022-054","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T15:59:15Z","timestamp":1722959955000},"page":"83-99","source":"Crossref","is-referenced-by-count":30,"title":["Using Artificial Intelligence in ESG Assurance"],"prefix":"10.2308","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9898-5017","authenticated-orcid":false,"given":"Nichole","family":"Li","sequence":"first","affiliation":[{"name":"Rutgers, The State University of New Jersey, Newark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3973-782X","authenticated-orcid":false,"given":"Meehyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Rutgers, The State University of New Jersey, Newark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0814-348X","authenticated-orcid":false,"given":"Jun","family":"Dai","sequence":"additional","affiliation":[{"name":"Michigan Technological University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miklos A.","family":"Vasarhelyi","sequence":"additional","affiliation":[{"name":"Rutgers, The State University of New Jersey, Newark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1112","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"2024100107514035200_B501","author":"AccountAbility","year":"2021"},{"key":"2024100107514035200_B1","doi-asserted-by":"crossref","unstructured":"Agbese,\n              M.\n            , R.Mohanani, A.Khan, and P.Abrahamsson. 2023. 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