Time to audit your AI algorithms

MAB Pub Date : 2022-09-16 DOI:10.5117/mab.96.90108
I. Sandu, Menno Wiersma, Daphne Manichand
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引用次数: 1

Abstract

Undoubtedly, the use of algorithms, and Artificial Intelligence (AI) algorithms in particular, has numerous benefits. Fields such as finance, healthcare, automotive, education, and recruitment, to name a few, have demonstrated successful application of AI algorithms. Conversely, cases of bad algorithms abound and lead to lost revenue, discrimination, disinformation, or even bodily harm. Currently, we have surpassed the stage of just observing bad algorithms. New European regulations governing AI force organizations to manage the risks introduced by algorithms and convince the public about the proper functioning of algorithms. In this context, can algorithms be rigorously audited to build public trust and if yes, how? This article aims to answer these questions by building on an auditing framework for model risk management that controls for the novelty introduced by AI algorithms while connecting AI algorithm audit with internal audit terminology.
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是时候审核你的人工智能算法了
毫无疑问,使用算法,尤其是人工智能算法,有很多好处。金融、医疗保健、汽车、教育和招聘等领域已经证明了人工智能算法的成功应用。相反,糟糕算法的案例比比皆是,并导致收入损失、歧视、虚假信息,甚至身体伤害。目前,我们已经超越了仅仅观察坏算法的阶段。欧洲管理人工智能的新法规迫使组织管理算法带来的风险,并说服公众相信算法的正确运行。在这种情况下,算法是否可以被严格审计以建立公众信任?如果可以,如何?本文旨在通过建立模型风险管理的审计框架来回答这些问题,该框架控制人工智能算法引入的新颖性,同时将人工智能算法审计与内部审计术语联系起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
MAB
MAB
自引率
0.00%
发文量
39
审稿时长
12 weeks
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