The advent and momentum gained by Generative AI erupted into the EU regulatory scene signalling a significant paradigm shift in the AI landscape. The AI Act has struggled to embrace the eruption and extraordinary popularity of Generative AI and managed to provide for specific solutions designed for these models. Nonetheless, there are legal and regulatory implications of Generative AI that may exceed the proposed solutions. Understanding the paradigm shift that Generative AI is likely to bring will allow us to assess the sufficiency and adequacy of the measures adopted and to identify possible shortcomings and gaps in the current EU framework. Generative AI raises specific problems in the compliance of AI Act obligations and in the application of liability rules that have to be acknowledged and properly addressed. Multimodality, emergence factor, scalability or generality of tasks may mismatch the assumption underlying the obligations and requirements laid down for AI systems. The chapter explores whether the current ecosystem of existing and still-to-be adopted rules on AI systems does fully and adequately address the distinctive features of Generative AI, with special consideration to the interaction between the AI Act and the liability rules as provided for the draft AILD and the revPLD.

As its name indicates, algorithmic regulation relies on the automation of regulatory processes through algorithms. Examining the impact of algorithmic regulation on the rule of law hence first requires an understanding of how algorithms work. In this chapter, I therefore start by focusing on the technical aspects of algorithmic systems (Section 2.1), and complement this discussion with an overview of their societal impact, emphasising their societal embeddedness and the consequences thereof (Section 2.2). Next, I examine how and why public authorities rely on algorithmic systems to inform and take administrative acts, with special attention to the historical adoption of such systems, and their impact on the role of discretion (Section 2.3). Finally, I draw some conclusions for subsequent chapters (Section 2.4).

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.

The potential of AI solutions to enhance effective decision-making, reduce costs, personalise offers and products, and improve risk management have not gone unnoticed by the financial industry. On the contrary, the characteristics of AI systems seem to perfectly accommodate to the features of financial services and to masterly address their most distinctive and challenging needs. Thus, the financial industry proves to provide a receptive and conducive environment to the growing application of AI solutions in a variety of tasks, activities, and decision-making processing. The aim of this paper is to examine the current state of the legal regime applicable in the European Union to the use of AI systems in the financial sector and to reflect on the need to formulate principles and rules that ensure responsible automation of decision-making and that serve as a guide for widely and extensively implementing AI solutions in banking activity.

The author spells out the different key features of AI systems, introducing inter alia the notions of machine learning and deep learning as well as the use of AI systems as part of robotics.