Explainable AI under contract and tort law: legal incentives and technical challenges
Explainable AI under contract and tort law: legal incentives and technical challenges
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DOI：
10.1007/s10506-020-09260-6
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发表时间：
2020-01-19
影响因子：
4.1
通讯作者：
Naumann, Felix
中科院分区：
文献类型：
--
作者：
Hacker, Philipp;Krestel, Ralf;Naumann, Felix
这篇论文表明，该法律可能以微妙的方式为采用可解释的机器学习应用程序设定了迄今尚未被认识到的激励措施。在这样做的时候，我们做出了两个新的贡献。首先，在法律的方面，我们表明，为了避免责任，专业人士，如医生和经理，可能很快就会在法律上被迫使用可解释的ML模型。我们认为，可解释性的重要性远远超出了数据保护法，并对使用ML模型的合同和侵权责任问题产生了至关重要的影响。为此，我们进行了两个法律的案例研究，在医疗和企业合并的ML应用。作为第二个贡献，我们讨论了（法律要求的）准确性和可解释性之间的权衡，并在垃圾邮件分类的背景下，在一个技术案例研究中展示了效果。
This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the (legally required) trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification.