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This paper introduces reviewability as a framework for improving theaccountability of automated and algorithmic decision-making (ADM) involvingmachine learning. We draw on an understanding of ADM as a socio-technicalprocess involving both human and technical elements, beginning before adecision is made and extending beyond the decision itself. While explanationsand other model-centric mechanisms may assist some accountability concerns,they often provide insufficient information of these broader ADM processes forregulatory oversight and assessments of legal compliance. Reviewabilityinvolves breaking down the ADM process into technical and organisationalelements to provide a systematic framework for determining the contextuallyappropriate record-keeping mechanisms to facilitate meaningful review - both ofindividual decisions and of the process as a whole. We argue that areviewability framework, drawing on administrative law's approach to reviewinghuman decision-making, offers a practical way forward towards more a moreholistic and legally-relevant form of accountability for ADM.

Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems