The Impoverished Publicness of Algorithmic Decision Making

Abstract

Abstract The increasing use of machine learning (ML) in public administration requires that we think carefully about the political and legal constraints imposed on public decision making. These developments confront us with the following interrelated questions: can algorithmic public decisions be truly ‘public’? And, to what extent does the use of ML models compromise the ‘publicness’ of such decisions? This article is part of a broader inquiry into the myriad ways in which digital and AI technologies transform the fabric of our democratic existence by mutating the ‘public’. Focusing on the site of public administration, the article develops a conception of publicness that is grounded in a view of public administrations as communities of practice. These communities operate through dialogical, critical and synergetic interactions that allow them to track—as faithfully as possible—the public’s heterogeneous view of its interests, and reify these interests in decision making. Building on this theorisation, the article suggests that the use of ML models in public decision making inevitably generates an impoverished publicness, and thus undermines the potential of public administrations to operate as a locus of democratic construction. The article thus advocates for a reconsideration of the ways in which administrative law problematises and addresses the harms of algorithmic decision making.