Large Language Models (LLMs) could facilitate both more efficient administrative decision-making on the one hand, and better access to legal explanations and remedies to individuals concerned by administrative decisions on the other hand. However, it is an open research question of how performant such domain-specific models could be. Furthermore, they pose legal challenges, touching especially upon administrative law, fundamental rights, data protection law, AI regulation, and copyright law. The article provides an introduction into LLMs, outlines potential use cases for such models in the context of administrative decisions, and presents a non-exhaustive introduction to practical and legal challenges that require in-depth interdisciplinary research. A focus lies on open practical and legal challenges with respect to legal reasoning through LLMs. The article points out under which circumstances administrations can fulfil their duty to provide reasons with LLM-generated reasons. It highlights the importance of human oversight and the need to design LLM-based systems in a way that enables users such as administrative decision-makers to effectively oversee them. Furthermore, the article addresses the protection of training data and trade-offs with model performance, bias prevention and explainability to highlight the need for interdisciplinary research projects.

This paper explores the necessary adaptations to the theory of administrative discretion when using AI systems. Regulatory frameworks in the EU, US, and Spain do not prohibit the application of AI in discretionary decision-making. Particularly, AI systems can be used when discretionary power involves correlations. However, to meet Rule of Law conditions, it is essential to establish adaptations and boundaries in areas such as duty of care, reason-giving, and judicial review. These conditions should focus on the impact of decisions on the affected individuals.

This article examines the regulatory landscape of financial data access within the European Union, emphasising the implications and effectiveness of recent legislative initiatives. It provides an in-depth analysis of the Financial Data Access Regulation (FIDA) proposal and its relationship with existing laws, focusing on the possibilities for data access and utilisation in the context of Open Finance. The discussion evaluates the regulatory framework for automated decision-making (ADM) in European financial law, highlighting its strengths and identifying areas for improvement. By exploring the intersections of tech resilience, financial regulation, and data protection, the article aims to clarify the practical implications of current and proposed rules, emphasising effective practices and areas needing further legislative attention.

This Article addresses the pressing issues surrounding the use of automated systems in public decision-making, specifically focusing on migration, asylum, and mobility. Drawing on empirical data, this Article examines the potential and limitations of the General Data Protection Regulation and the Artificial Intelligence Act in effectively addressing the challenges posed by automated decision-making (ADM). The Article argues that the current legal definitions and categorizations of ADM fail to capture the complexity and diversity of real-life applications where automated systems assist human decision-makers rather than replace them entirely. To bridge the gap between ADM in law and practice, this Article proposes to move beyond the concept of “automated decisions” and complement the legal protection in the GDPR and AI Act with a taxonomy that can inform a fundamental rights analysis. This taxonomy enhances our understanding of ADM and allows to identify the fundamental rights at stake and the sector-specific legislation applicable to ADM. The Article calls for empirical observations and input from experts in other areas of public law to enrich and refine the proposed taxonomy, thus ensuring clearer conceptual frameworks to safeguard individuals in our increasingly algorithmic society.

Automated decision-making takes up an increasingly significant place in the administrative state. This article presents a conception of discretion that is helpful for evaluating the proper place of algorithms in public decision-making. I argue that the algorithm itself is not a site of discretion. The threat is that automated decision-making alters the relationships between traditional actors in a way that can cut down discretion and human commitment. Algorithmic decision-makers can serve to fetter the discretion that the legislature and the populace expect to be exercised. We must strive to maintain discretion, moral agency, deliberative ideals, and human commitment through the system that surrounds the use of an algorithm and to develop a new expertise that can retain and exercise the expected discretion. Backing this argument are traditional legal constraints, public expectations, and administrative law principles, tied together through the organizing principle of discretion.

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain why this systemic exclusion is of moral concern and to offer a solution to address it.

The future is likely to see an increase in the public-sector use of automated decision-making systems which employ machine learning techniques. However, there is no clear understanding of how English administrative law will apply to this kind of decision-making. This paper seeks to address the problem by bringing together administrative law, data protection law, and a technical understanding of automated decision-making systems in order to identify some of the questions to ask and factors to consider when reviewing the use of these systems. Due to the relative novelty of automated decision-making in the public sector, this kind of study has not yet been undertaken elsewhere. As a result, this paper provides a starting point for judges, lawyers, and legal academics who wish to understand how to legally assess or review automated decision-making systems and identifies areas where further research is required.

This paper examines the application of the latest iterations of EU data protection law – in the General Data Protection Regulation, the Law Enforcement Directive and the jurisprudence of the Court of Justice of the EU – to the use of predictive policing technologies. It suggests that the protection offered by this legal framework to those impacted by predictive policing technologies is, at best, precarious. Whether predictive policing technologies fall within the scope of the data protection rules is uncertain, even in light of the expansive interpretation of these rules by the Court of Justice of the EU. Such a determination would require a context-specific assessment that individuals will be ill-placed to conduct. Moreover, even should the rules apply, the substantive protection offered by the prohibition against automated decision-making can be easily sidestepped and is subject to significant caveats. Again, this points to the conclusion that the protection offered by this framework may be more illusory than real. This being so, there are some fundamental questions to be answered – including the question of whether we should be building predictive policing technologies at all.