Algorithmic Decision-Making and System Destructiveness: A Case of Automatic Debt Recovery

Abstract

Governments are increasingly relying on algorithmic decision-making (ADM) to deliver public services. Recent information systems literature has raised concerns regarding ADM’s negative unintended consequences, such as widespread discrimination, which in extreme cases can be destructive to society. The extant empirical literature, however, has not sufficiently examined the destructive effects of governmental ADM. In this paper, we report on a case study of the Australian government’s “Robodebt” programme that was designed to automatically calculate and collect welfare overpayment debts from citizens but ended up causing severe distress to citizens and welfare agency staff. Employing perspectives from systems thinking and organisa- tional limits, we develop a research model that explains how a socially destructive government ADM programme was initiated, sustained, and delegitimized. The model offers a set of gen- eralisable mechanisms that can benefit investigations of ADM’s consequences. Our findings contribute to the literature of unintended consequences of ADM and demonstrate to practi- tioners the importance of setting up robust governance infrastructures for ADM programmes.