Reviewable Automated Decision-Making
Reviewable Automated Decision-Making
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DOI：
10.1016/j.clsr.2020.105475
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发表时间：
2020-11-01
影响因子：
2.9
通讯作者：
Singh, Jatinder
中科院分区：
文献类型：
--
作者：
Cobbe, Jennifer;Singh, Jatinder
在本文中，我们引入了“可审查性”的概念，作为一种替代方法，以改善涉及机器学习系统的自动决策的问责制。在这样做的过程中，我们借鉴了自动化决策作为一个社会技术过程的理解，涉及人类（组织）和技术组件，从决策之前开始，并延伸到决策本身之外。尽管对自动化决策的解释在某些情况下可能有用，但它们更狭隘地关注模型，因此没有提供有关整个流程的信息，而这些信息对于问责制、监管监督和法律的合规性评估的许多方面都是必要的。借鉴以前的工作，行政法和司法审查机制的应用，以自动化决策在公共部门，我们认为，打破自动化决策过程中的技术和组织的组成部分，使我们能够考虑如何适当的记录保存和日志机制，在该过程的每个阶段实施将允许整个过程进行审查。虽然需要大量的研究来探索如何实施，但我们认为，与更狭隘地关注解释的方法相比，可审查性框架可能为自动化决策提供更有用、更全面的问责形式。(C)2020年詹妮弗·科布和贾廷德·辛格由爱思唯尔有限公司出版。保留所有权利。
In this paper we introduce the concept of `reviewability' as an alternative approach to im-proving the accountability of automated decision-making that involves machine learning systems. In doing so, we draw on an understanding of automated decision-making as a socio-technical process, involving both human (organisational) and technical components, beginning before a decision is made and extending beyond the decision itself. Although explanations for automated decisions may be useful in some contexts, they focus more narrowly on the model and therefore do not provide the information about that process as a whole that is necessary for many aspects of accountability, regulatory oversight, and assessments for legal compliance. Drawing on previous work on the application of administrative law and judicial review mechanisms to automated decision-making in the public sector, we argue that breaking down the automated decision-making process into its technical and organisational components allows us to consider how appropriate record-keeping and logging mechanisms implemented at each stage of that process would allow for the process as a whole to be reviewed. Although significant research is needed to explore how it can be implemented, we argue that a reviewability framework potentially offers for a more useful and more holistic form of accountability for automated decision-making than approaches focused more narrowly on explanations. (C) 2020 Jennifer Cobbe and Jatinder Singh. Published by Elsevier Ltd. All rights reserved.