Procedural Fairness as Stepping Stone for Successful Implementation of Algorithmic Decision-Making in Public Administration: Review and Outlook

Abstract

Algorithmic decision-making (ADM) is becoming more and more prevalent in everyday life. Due to their promise of producing faster, better, and less biased decisions, automated and data-driven processes also receive increasing attention in many different administrative settings. However, as a result of human mistakes ADM also poses the threat of producing unfair outcomes. Looming algorithmic discrimination can undermine the legitimacy of administrative decision-making. While lawyers and lawmakers face the age-old question of regulation, many decision-makers tasked with designing ADM for and implementing ADM in public administration wrestle with harnessing its advantages and limiting its disadvantages. “Algorithmic fairness” has evolved as key concept in developing algorithmic systems to counter detrimental outcomes. We provide a review of the vast literature on algorithmic fairness and show how key dimensions alter people’s perception of whether an algorithm is fair. In doing so, we provide entry point into this literature for anybody who is required to think about algorithmic fairness, particularly in an public administration context. We also pinpoint critical concerns about algorithmic fairness that public officials and researchers should note.