Responsible and accountable algorithmization

Abstract

Government bureaucracies are adopting machine-learning algorithms to aid their decision-making. These processes of algorithmization – organizational change driven by the introduction of algorithms – may undermine citizen trust. Concerning the algorithmization of government, various concerns have been raised regarding the impacts of these systems on privacy, but also on discrimination against different groups in society and its neglect of human contact. Such issues violate basic assumptions of procedural fairness, which, in turn, may hamper citizen trust in government. We argue that both developers of algorithms and policymakers need to invest in responsible and accountable algorithmization of traditional bureaucratic decision-making in order to sustain procedural fairness and, eventually, trust in government decisions. Based on literatures on responsibility and accountability, we identify assessment questions that can guide public organizations in the use of algorithms.

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