Benjamin P. Commerford, Aasmund Eilifsen, Richard C. Hatfield, Kathryn M. Holmstrom, Finn Kinserdal
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引用次数: 0
Abstract
In this study, we examine auditors' reliance on artificial intelligence (AI) systems that are designed to provide evidence around complex estimates. In an experiment with highly experienced auditors, we find that auditors are more hesitant to rely on evidence from AI‐based systems compared to human specialists, consistent with algorithm aversion. Importantly, we also find that a small amount of control (i.e., providing input to specialists) can mitigate this aversion, though this effect depends on auditors' personal locus of control (LOC). Providing input increases reliance on evidence from AI systems for auditors who believe they have little control over their outcomes (i.e., an external LOC). In contrast, auditors with an internal LOC are particularly hesitant to rely on AI‐based evidence, and providing input has little impact on their reliance. Interviews with experienced auditors corroborate our findings and suggest auditors feel a greater sense of control working with human specialists relative to AI‐based systems. Overall, our results suggest perceived control plays an important role in auditors' aversion to AI and that auditors' individual traits can affect this aversion.
在本研究中,我们考察了审计师对人工智能(AI)系统的依赖性,这些系统旨在提供有关复杂估算的证据。在一项以经验丰富的审计师为对象的实验中,我们发现与人类专家相比,审计师在依赖人工智能系统提供的证据时更加犹豫不决,这与算法厌恶是一致的。重要的是,我们还发现少量的控制(即向专家提供输入)可以减轻这种厌恶感,尽管这种效果取决于审计人员的个人控制点(LOC)。对于那些认为自己对审计结果几乎没有控制权的审计人员(即外部控制点)来说,提供投入会增加他们对人工智能系统提供的证据的依赖性。与此相反,具有内部 LOC 的审计师在依赖基于人工智能的证据时尤其犹豫不决,而提供意见对他们的依赖性影响甚微。对经验丰富的审计师进行的访谈证实了我们的研究结果,并表明与人工智能系统相比,审计师在与人类专家合作时会有更强的控制感。总之,我们的研究结果表明,感知控制在审计师对人工智能的反感中起着重要作用,而审计师的个人特质会影响这种反感。
期刊介绍:
Contemporary Accounting Research (CAR) is the premiere research journal of the Canadian Academic Accounting Association, which publishes leading- edge research that contributes to our understanding of all aspects of accounting"s role within organizations, markets or society. Canadian based, increasingly global in scope, CAR seeks to reflect the geographical and intellectual diversity in accounting research. To accomplish this, CAR will continue to publish in its traditional areas of excellence, while seeking to more fully represent other research streams in its pages, so as to continue and expand its tradition of excellence.