可审查的自动化决策：一个可问责的决策系统框架
Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems
摘要：
本文介绍了可审查性作为一个框架，以提高问责制的自动化和算法决策（ADM）涉及机器学习。我们利用ADM的理解，作为一个社会技术过程，涉及人力和技术因素，开始之前作出决定，并超出了决定本身。虽然解释和其他以模型为中心的机制可能有助于一些问责问题，但它们通常提供的这些更广泛的ADM流程的信息不足，无法用于监管监督和法律合规性评估。可审查性涉及将ADM流程分解为技术和组织要素，以提供一个系统框架，用于确定适合背景的记录保存机制，以促进对个人决策和整个流程的有意义的审查。我们认为，可审查性框架，借鉴行政法的方法来审查人类的决策，提供了一个切实可行的方法，朝着更全面和法律相关的形式问责ADM。
英文摘要：
This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
