Social welfare has long been a priority area for digitisation and more recently for ADM. Digitisation and ADM can either advance or threaten socio-economic rights of the marginalised. Current Australian examples include the roll-out of on-line and apps-based client interfaces and compliance technologies in Centrelink. Others include work within the National Disability Insurance Scheme (NDIS) on development of virtual assistants or use of AI to leverage existing data sets to aid or displace human decision-making. Drawing on these examples and other recent experience, this chapter reviews the adequacy of traditional processes of public policy development, public administration, and legal regulation/redress in advancing and protecting the socio-economic rights of the marginalised in the rapidly emerging automated welfare state. It is argued that protections are needed against the power of ADM to collapse program design choices so that outliers, individualisation, complexity, and discretion are excluded or undervalued. It is suggested that innovative new processes may be needed, such as genuine co-design and collaborative fine-tuning of ADM initiatives, new approaches to (re)building citizen trust and empathy in an automated welfare state, and creative new ways of ensuring equal protection of the socio-economic rights of the marginalised in social services and responsiveness to user interests.

Chapter 7 analyses the legal challenges that incorporation of AI-systems in the Automated State will bring. The starting point is that legal systems have coped relatively well so far with the use of computers by public authorities. The critical disruption of the Automated State predicted by Robert McBride in 1967 has not been materialised and, therefore, we have not been forced to substantively rethink the adequacy of how administrative law deals with machines. However, the incorporation of AI in automation may be that disruption. In this chapter, Bello y Villarino offers a counterpoint to those who believe that existing principles and rules can be easily adapted to address the use of AI in the public sector. He discusses the distinct elements of AI, through an exploration of the dual role of public authorities: a state that executes policy and a state that designs policy. The use of AI systems in both contexts are of a different regulatory order. Until now there has been an assumption that policy design should be allowed a broad margin of discretion, especially when compared to the state as an executor of policies and rules. Yet, the automation of policy design will require that public authorities make explicit decisions about objectives, boundary conditions, and preferences. Discretion for humans can remain, but AI systems analysing policy choices may suggest that certain options are superior to others. This could justify employing different legal lenses to approach the regulation of automated decision-making and decision-support systems used by the State. The reasoning, to some extent, could also be extrapolated to Automated Banks. Each perspective is analysed in reference to the activity of modern states. The main argument is that the AI-driven Automated State is not suited for the one-size-fits-all approach often claimed to apply to administrative law. The final part of the chapter explores some heuristics that could facilitate the regulatory transition.