Digital Discretion: Unpacking Human and Technological Agency in Automated Decision Making in Sweden’s Social Services

Abstract

The introduction of robotic process automation (RPA) into the public sector has changed civil servants’ daily life and practices. One of these central practices in the public sector is discretion. The shift to a digital mode of discretion calls for an understanding of the new situation. This article presents an empirical case where automated decision making driven by RPA has been implemented in social services in Sweden. It focuses on the aspirational values and effects of the RPA in social services. Context, task, and activities are captured by a detailed analysis of humans and technology. This research finds that digitalization in social services has a positive effect on civil servants’ discretionary practices mainly in terms of their ethical, democratic, and professional values. The long-term effects and the influence on fair and uniform decision making also merit future research. In addition, the article finds that a human–technology hybrid actor redefines social assistance practices. Simplifications are needed to unpack the automated decision-making process because of the technological and theoretical complexities.

Studying social policy in the digital age

“…A similar trend is visible for welfare services, where digitalized processes and machine learning are becoming an integral part of public administration and much scholarly attention is now geared to analysing 'the digital by design' Universal Credit reform, where the United Kingdom's welfare claim and provision was fully automated in 2013 (Millar & Bennett, 2017). Much scholarly attention has also been paid to analysing Denmark, where automated decision-making systems currently process complex tasks, even those requiring in-depth discretion (Henriksen, 2018;Schou & Pors, 2019;Ranerup & Henriksen, 2020). Research shows that although technical systems (are expected) to cut down tedious tasks (Schou & Pors, 2019), they are generally driven by efficiency and budgetary considerations and allow more forceful monitoring and stigmatization.…”

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

“…Moreover, research on representative bureaucracy shows that advisors’ decision-making can be influenced by unconscious biases and racial and class stereotypes (Harrits, 2019; Soss et al, 2011). In comparison, ADM is positioned as ‘safeguard[ing] fair and uniform decision-making’ through stricter ‘adherence to rules and procedures’ (Ranerup and Henriksen, 2020: 11). Casey observes how this is a key motivation behind Australia’s online employment services model, framing it as an extension of long-standing government efforts to achieve greater ‘adherence to policy intent’ (2021: 4).…”

Artificial Intelligence in Social Security: Opportunities and Challenges

“…Now it is possible to apply for benefits online, and 73,5 % of applications to Kela were filed online in 2020, which is an increase from 2016 when 64 % applied online (Kela 2021). In other cases, a recent report from the Trelleborg Swedish municipality states that 85 % of the digital applications for social assistance are handled at least partly by the RPA (information and calculation) and 30 % are handled entirely by the RPA (Ranerup, Henriksen 2020). The Finnish Centre for Pensions also tested the machine learning algorithm on the centre's anonymous register data of 500,000 people, correctly predicting 78 % of future retirees who were set to retire on a disability pension in two years (Theo 2018).…”