Automation scenarios: citizen attitudes towards automated decision-making in the public sector

Abstract

ABSTRACT This article explores citizen attitudes towards automated decision-making (ADM) in the public sector, addressing concerns related to social justice and transparency. ADM, used in diverse public services, such as benefit application processing, welfare fraud detection and tax calculation, has sparked public interest and scepticism. To shed light on this complex issue and make ADM more accessible for citizens, we presented three domain-specific scenarios in a population-representative survey in Estonia (n = 1,500), Germany (n = 2,001) and Sweden (n = 1,000). These scenarios involved job seeker categorisation, child welfare risk assessment and predictive policing through facial recognition. Drawing from this survey and adopting an exploratory approach, we analyse attitudes across responses to these scenarios and conduct a regression analysis, integrating individual variables such as age, gender, education, awareness, enthusiasm and trust in ADM systems. Our findings reveal differences in citizens’ attitudes based on welfare regimes and individual characteristics. This citizen-focused approach underscores the significance of involving citizens in the governance of ADM in the digital welfare state, transcending the traditional regulatory and stakeholder-centric perspectives.