New Scientist changed the UK's freedom of information laws in 2025 By requesting copies of the then-UK technology secretary's ChatGPT logs, New Scientist set a precedent for how freedom of information laws apply to chatbot interactions, helping to hold governments to account Our successful request for Peter Kyle's ChatGPT logs stunned observers When I fired off an email at the start of 2025, I hadn't intended to set a legal precedent for how the UK government handles its interactions with AI chatbots, but that is exactly what happened. It all began in January when I read an interview with the then-UK tech secretary Peter Kyle in . Trying to suggest he used first-hand the technology his department was set up to regulate, Kyle said that he would often have conversations with ChatGPT. AI may blunt our thinking skills - here's what you can do about it That got me wondering: could I obtain his chat history? Freedom of information (FOI) laws are often deployed to obtain emails and other documents produced by public bodies, but past precedent has suggested that some private data - such as search queries - aren't eligible for release in this way. I was interested to see which way the chatbot conversations would be categorised.

Legal Judgment Prediction (LJP) has emerged as a key area in AI for law, aiming to automate judicial outcome forecasting and enhance interpretability in legal reasoning. While previous approaches in the Indian context have relied on internal case content such as facts, issues, and reasoning, they often overlook a core element of common law systems, which is reliance on statutory provisions and judicial precedents. In this work, we propose NyayaRAG, a Retrieval-Augmented Generation (RAG) framework that simulates realistic courtroom scenarios by providing models with factual case descriptions, relevant legal statutes, and semantically retrieved prior cases. NyayaRAG evaluates the effectiveness of these combined inputs in predicting court decisions and generating legal explanations using a domain-specific pipeline tailored to the Indian legal system. We assess performance across various input configurations using both standard lexical and semantic metrics as well as LLM-based evaluators such as G-Eval. Our results show that augmenting factual inputs with structured legal knowledge significantly improves both predictive accuracy and explanation quality.

Chalkidis et al. ( 2019) introduces the ECtHR dataset that consists of 11k cases from the European Court of Human Rights. Niklaus et al. ( 2021) releases the Swiss-Judgements-Prediction dataset that consists of 85k multilingual cases-German, French, and Italian-from the Federal Supreme Court of Switzerland. Xiao et al. ( 2018) introduces the CAIL dataset which consists of 2.7m Chinese criminal cases. The court debates are not publicly available in Korea. Chalkidis et al. ( 2022a) introduces a benchmark dataset for legal NLU in English focusing on Chalkidis et al. ( 2022b) investigate legal fairness over four legal judgement datasets with additional A.2 Precedent redaction rule Data subjected to anonymization are as follows Other personally identifible information: Social security number is deleted.

The LJP tasks consist of (1) 10.5k criminal examples where the model is

Songs used by ChatGPT included Herbert Grönemeyer's 1984 synth-pop sendup of masculinity, ' (Men). Songs used by ChatGPT included Herbert Grönemeyer's 1984 synth-pop sendup of masculinity, ' (Men). OpenAI ordered to pay undisclosed damages for training its language models on artists' work without permission The Munich regional court sided in favour of Germany's music rights society GEMA, which said ChatGPT had harvested protected lyrics by popular artists to "learn" from them. The collecting society GEMA, which manages the rights of composers, lyricists and music publishers and has approximately 100,000 members, filed the case against OpenAI in November 2024. The lawsuit was seen as a key European test case in a campaign to stop AI scraping of creative output.

Identifying/retrieving relevant statutes and prior cases/precedents for a given legal situation are common tasks exercised by law practitioners. Researchers to date have addressed the two tasks independently, thus developing completely different datasets and models for each task; however, both retrieval tasks are inherently related, e.g., similar cases tend to cite similar statutes (due to similar factual situation). In this paper, we address this gap. We propose IL-PCR (Indian Legal corpus for Prior Case and Statute Retrieval), which is a unique corpus that provides a common testbed for developing models for both the tasks (Statute Retrieval and Precedent Retrieval) that can exploit the dependence between the two. We experiment extensively with several baseline models on the tasks, including lexical models, semantic models and ensemble based on GNNs. Further, to exploit the dependence between the two tasks, we develop an LLM-based re-ranking approach that gives the best performance.

Large Language Models (LLMs) are increasingly explored for legal argument generation, yet they pose significant risks of manipulation through hallucination and ungrounded persuasion, and often fail to utilize provided factual bases effectively or abstain when arguments are untenable. This paper introduces a novel reflective multi-agent method designed to address these challenges in the context of legally compliant persuasion. Our approach employs specialized agents (factor analyst and argument polisher) in an iterative refinement process to generate 3-ply legal arguments (plaintiff, defendant, rebuttal). We evaluate reflective multi-agent against single-agent, enhanced-prompt single-agent, and non-reflective multi-agent baselines using four diverse LLMs (GPT-4o, GPT-4o-mini, Llama-4-Maverick-17b-128e, Llama-4-Scout-17b-16e) across three legal scenarios: "arguable", "mismatched", and "non-arguable". Results demonstrate that the reflective multi-agent approach excels at successful abstention by preventing generation when arguments cannot be grounded, improves hallucination accuracy by reducing fabricated and misattributed factors and enhances factor utilization recall by better using the provided case facts. These findings suggest that structured reflection within a multi-agent framework offers a robust method for fostering ethical persuasion and mitigating manipulation in LLM-based legal argumentation systems.

Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system.

Learning-based autonomous driving systems are trained mostly on incident-free data, offering little guidance near safety-performance boundaries. Real crash reports contain precisely the contrastive evidence needed, but they are hard to use: narratives are unstructured, third-person, and poorly grounded to sensor views. We address these challenges by normalizing crash narratives to ego-centric language and converting both logs and crashes into a unified scene-action representation suitable for retrieval. At decision time, our system adjudicates proposed actions by retrieving relevant precedents from this unified index; an agentic counterfactual extension proposes plausible alternatives, retrieves for each, and reasons across outcomes before deciding. On a nuScenes benchmark, precedent retrieval substantially improves calibration, with recall on contextually preferred actions rising from 24% to 53%. The counterfactual variant preserves these gains while sharpening decisions near risk.

Legal judgment prediction (LJP) has become increasingly important in the legal field. In this paper, we identify that existing large language models (LLMs) have significant problems of insufficient reasoning due to a lack of legal knowledge. Therefore, we introduce GLARE, an agentic legal reasoning framework that dynamically acquires key legal knowledge by invoking different modules, thereby improving the breadth and depth of reasoning. Experiments conducted on the real-world dataset verify the effectiveness of our method. Furthermore, the reasoning chain generated during the analysis process can increase interpretability and provide the possibility for practical applications.