La Trobe
1197434_Considine,M_2022.pdf (242.89 kB)
Download file

Can Robots Understand Welfare? Exploring Machine Bureaucracies in Welfare-to-Work

Download (242.89 kB)
journal contribution
posted on 2022-07-21, 22:42 authored by Mark Considine, Michael McGann, Sarah Ball, Phuc NguyenPhuc Nguyen

The exercise of administrative discretion by street-level workers plays a key role in shaping citizens’ access to welfare and employment services. Governance reforms of social services delivery, such as performance-based contracting, have often been driven by attempts to discipline this discretion. In several countries, these forms of market governance are now being eclipsed by new modes of digital governance that seek to reshape the delivery of services using algorithms and machine learning. Australia, a pioneer of marketisation, is one example, proposing to deploy digitalisation to fully automate most of its employment services rather than as a supplement to face-to-face case management. We examine the potential and limits of this project to replace human-to-human with ‘machine bureaucracies’. To what extent are welfare and employment services amenable to digitalisation? What trade-offs are involved? In addressing these questions, we consider the purported benefits of machine bureaucracies in achieving higher levels of efficiency, accountability, and consistency in policy delivery. While recognising the potential benefits of machine bureaucracies for both governments and jobseekers, we argue that trade-offs will be faced between enhancing the efficiency and consistency of services and ensuring that services remain accessible and responsive to highly personalised circumstances.


Publication Date



Journal of Social Policy






519 - 534


Cambridge University Press



Rights Statement

© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.