Historically, literature suggests that a variety of accounting roles will be replaced by Artificial Intelligence (AI) and related technologies; however, in recent years there is a growing recognition that accounting can in fact harness AI’s potential to add value to organisations. Commentators have highlighted the need for increased research exploring accounting and AI and for accounting scholars to consider multi-disciplinary research in this area. This study uses a form of topic modelling to analyse literature exploring AI and related techniques in an accounting context. Latent Dirichlet Allocation (LDA) has been used to enable probabilistic, machine-based interrogation of large volumes of literature. This study applies LDA to the abstracts of 930 peer-reviewed academic publications from a variety of disciplines to identify the most significant accounting and AI topics discussed in the literature during the period 1990 to 2023. Our findings suggest that prior literature reviews based on more traditional methodologies do not capture a comprehensive picture of accounting and AI research. Eleven topic clusters are identified which provide a comprehensive topology of the extant literature discussing accounting and AI and set out an agenda for future research designed to foster academic progress in the area. It also represents one of the first applications of probabilistic topic modelling to accounting literature.

中文翻译：

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使用主题建模探索会计和人工智能


从历史上看，文献表明各种会计角色将被人工智能（AI）和相关技术所取代；然而，近年来，人们越来越认识到会计实际上可以利用人工智能的潜力为组织增加价值。评论家强调需要加强对会计和人工智能的研究，以及会计学者考虑该领域的多学科研究。本研究使用主题建模的形式来分析在会计背景下探索人工智能和相关技术的文献。潜在狄利克雷分配 (LDA) 已用于实现对大量文献进行基于机器的概率询问。本研究将 LDA 应用到来自不同学科的 930 篇经过同行评审的学术出版物的摘要中，以确定 1990 年至 2023 年期间文献中讨论的最重要的会计和人工智能主题。我们的研究结果表明，之前的文献综述基于更传统的方法论并不能全面反映会计和人工智能研究的情况。确定了十一个主题群，提供了讨论会计和人工智能的现有文献的全面拓扑，并为旨在促进该领域学术进步的未来研究制定了议程。它还代表了概率主题建模在会计文献中的首批应用之一。