[{"source":"Scoop.it - Documentation juridique (Stéphan Cottin)","description":"This article belongs to the debate » Thinking Machines, Thinking Lawyers14 October 2026The (Currently) Uneven Intelligence of AI in Comparative Constitutional Law From Years of Research to a Single Prompt? Artificial intelligence (AI) has the potential to accelerate and improve the quality of research. This potential has inspired numerous projects exploring how AI could help scholarship on constitutional law. For example, there is research using AI to interpret constitutions,1) apply constitutional principles,2) and predict court rulings.3) Even the question of whether AI could improve the process of constitution-making has been studied.4) AI’s potential is particularly significant for comparative constitutional research. Systematically comparing legal systems requires locating sources from different jurisdictions, which are often difficult to access and only available in local languages. The creation of comparative legal datasets has thus involved considerable personnel, time, and financial resources. AI can overcome many of these obstacles. AI models can search for and translate foreign-language sources, filter them, and convert the filtered information into a dataset. In doing so, AI offers the prospect of datasets — which previously required years of research and the efforts of numerous experts and research assistants to compile — being just a prompt away. However, whether AI models can generate comparative law datasets that are qualitatively comparable to human-coded datasets has not yet been addressed (although Guha et al provide an example for a broader legal benchmarking exercise).5) We therefore conducted our own benchmarking study.6) We found evidence suggesting that recent AI models may be able to code simple factual data, but they are likely unreliable substitutes for expert human coders when conceptual or interpretive judgment is required. The Benchmarking Project: Seven Coding Tasks, Four Datasets, Two LLMs In our project, we tested the ability of two large language models (LLMs) to code different types of information relevant to comparative constitutional law. We specifically assessed whether LLMs can reproduce information from four existing comparative constitutional law datasets, all of which were compiled through human coding and which served as a ‘ground truth’ for our exercise. From these datasets, we asked the LLMs to code seven variables, which can be thought of as seven tasks relevant to the creation of comparative constitutional law datasets. Each of these tasks is either a test of fact-finding or reasoning abilities. First, from the Executive Term Limit Evasion Dataset,7) we drew information on whether presidents in 106 presidential systems have evaded constitutionally mandated term limits between 2000 and 2019. The dataset includes one variable on whether presidents attempt to overstay past the end of their term. The second variable captures what strategies executives use for doing so, such as amending the constitution, replacing it with a new one, or asking the court to interpret term limits out of the constitution. While determining whether a president overstayed is a fact-finding task, assessing the strategy used to do so requires reasoning. Second, from the Comparative Constitution Making Dataset,8) we drew information on the process by which constitutions were adopted in democracies between 1900 and 2015. This dataset also includes fact-finding variables such as (1) whether citizens were involved in the formulation, discussion, or submission of reform proposals, and (2) whether citizens participated in popular referendums at any stage in the process. The dataset’s most important variable (3) captures whether constitutions reflect ‘plural elite agreement,’ that is, whether all relevant elite factions in society were involved and represented during the process. The recreation of this variable requires reasoning. Third, from the Characteristics of National Constitutions Dataset9), we drew on its coding of 43 first-generation constitutional","id":3218421,"titre":"The (Currently) Uneven Intelligence of AI in Comparative Constitutional Law","url":"https://verfassungsblog.de/the-currently-uneven-intelligence-of-ai-in-comparative-constitutional-law/","pubDate":"Thu, 15 Oct 2026 08:26:22 +0200"},{"id":2850552,"description":"Brussels, 27 April – Today marks the 10th anniversary of the GDPR’s adoption, the first comprehensive data protection framework spanning an entire continent, establishing clear rights for individuals and obligations for organisations across Europe. The moment that led to the creation of the EDPB The GDPR led to the establishment of the European Data Protection Board (EDPB) on 25 May 2018, replacing the Article 29 Working Party that was previously in charge of dealing with issues relating to the protection of personal data. The GDPR gave the Data Protection Authorities (DPAs) stronger enforcement powers and expanded the scope of their work from focusing mainly on national compliance complaints to routinely dealing with cross-border cases. In the past 10 years, the 31 European DPAs comprising the EDPB have worked together to ensure the consistent enforcement of the GDPR and a harmonised data protection approach across Europe.   A key role in an evolving digital landscape Today, the GDPR is part of a broader and evolving European digital framework, alongside other digital laws such as the Digital Services Act, the Digital Markets Act, and the AI Act . In a world shaped by artificial intelligence, platform economies, and increasing data-driven innovation, the GDPR ensures that technological progress goes hand in hand with the protection of individuals’ fundamental rights . An inspiration for the rest of the world The impact of the GDPR has extended far beyond Europe’s borders, inspiring similar frameworks across the globe and contributing to a growing international recognition of privacy as a fundamental right .   How the GDPR has shaped the data protection landscape: insights from Data Protection Authorities Have you ever wondered what the data protection landscape looked like before the GDPR and how DPAs prepared for its entry into force? How has life for Europeans changed since its adoption? Watch the video for insights and testimonies from Data Protection Authorities which contributed to the shaping of the data protection landscape in Europe.   Sorry, your browser doesn't support embedded","source":"EDPB News","pubDate":"Mon, 27 Apr 2026 14:14:32 +0200","url":"https://www.edpb.europa.eu/news/news/2026/marking-10-years-gdpr-evolution-european-data-protection-landscape_en","titre":"Marking 10 years of the GDPR: the evolution of the European data protection landscape"},{"description":"Key Takeaways: Armalytix: A Driving Force in Property Transactions In a groundbreaking development, Armalytix, the leading UK-based data intelligence firm specializing in source of funds and affordability checks, has emerged as the driving force behind legal firms responsible for 1 in 8 residential property transactions conducted in England and Wales. Within just two years since its market introduction, Armalytix’s technology has rapidly evolved into the foremost provider for legal firms engaged in property transactions. A Year of Success: Recognitions and Uptake Following a successful 2023 marked by nominations for Modern Law and the British Conveyancing Awards, Armalytix has witnessed a The post Revealed: The Secret Behind 1 in 8 Property Transactions in England and Wales! appeared first on Business Today","id":1184936,"source":"Business Today","titre":"Revealed: The Secret Behind 1 in 8 Property Transactions in England and Wales!","url":"https://businesstoday.news/revealed-the-secret-behind-1-in-8-property-transactions-in-england-and-wales/?utm_source=rss&utm_medium=rss&utm_campaign=revealed-the-secret-behind-1-in-8-property-transactions-in-england-and-wales","pubDate":"Thu, 14 Mar 2024 16:29:57 +0100"}]