'Organisation for Economic Co-Operation and Development (OECD)'
Doi
Abstract
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