La Trobe
2021_Can-robots-understand-welfare-exploring-machine-bureaucracies-in-welfare-to-work.pdf (247.2 kB)
Download file

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

Download (247.2 kB)
journal contribution
posted on 22.03.2022, 03:16 by Mark Considine, Michael McGann, Sarah Ball, Phuc NguyenPhuc Nguyen
Abstract 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.

History

Publication Date

16/03/2022

Journal

Journal of Social Policy

Pagination

(p. 1-16)

Publisher

Cambridge University Press (CUP)

ISSN

0047-2794

Rights Statement

©The Author(s), 2022. Published by Cambridge University Press. This is an OpenAccess article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction inany medium, provided the original work is properly cited. doi:10.1017/S0047279422000174

Usage metrics

Categories

Licence

Exports