Recent memoirs by the Justices reveal how a new vision of restraint has led to radical outcomes. To understand how grudging Amy Coney Barrett's new book is when it comes to revealing personal details, consider that one of the family members the Supreme Court Justice most often refers to is a great-grandmother who died five years before she was born. On Barrett's desk at home, she recounts in " Listening to the Law," she keeps a photograph of her great-grandmother's one-story house, where, as a widow during the Great Depression, she raised some of her thirteen children and took in other needy relatives. "Looking at the photo reminds me of a woman who stretched herself beyond all reasonable capacity," Barrett explains. "I'm not sure that I'll be able to manage my life with the same grace that she had. But she motivates me to keep trying." For Barrett, the mother of seven children, that effort entails setting her alarm for 5 "Our kids get up at six thirty during the school year, so I start early if I want to accomplish anything on my own to-do list," she writes. This is what passes for disclosure from Barrett; she measures out the details of her life with coffee spoons, careful not to spill.

Large language models are increasingly used for Islamic guidance, but risk misquoting texts, misapplying jurisprudence, or producing culturally inconsistent responses. We pilot an evaluation of GPT-4o, Ansari AI, and Fanar on prompts from authentic Islamic blogs. Our dual-agent framework uses a quantitative agent for citation verification and six-dimensional scoring (e.g., Structure, Islamic Consistency, Citations) and a qualitative agent for five-dimensional side-by-side comparison (e.g., Tone, Depth, Originality). GPT-4o scored highest in Islamic Accuracy (3.93) and Citation (3.38), Ansari AI followed (3.68, 3.32), and Fanar lagged (2.76, 1.82). Despite relatively strong performance, models still fall short in reliably producing accurate Islamic content and citations -- a paramount requirement in faith-sensitive writing. GPT-4o had the highest mean quantitative score (3.90/5), while Ansari AI led qualitative pairwise wins (116/200). Fanar, though trailing, introduces innovations for Islamic and Arabic contexts. This study underscores the need for community-driven benchmarks centering Muslim perspectives, offering an early step toward more reliable AI in Islamic knowledge and other high-stakes domains such as medicine, law, and journalism.

Legal NLP remains underdeveloped in regions like India due to the scarcity of structured datasets. We introduce IndianBailJudgments-1200, a new benchmark dataset comprising 1200 Indian court judgments on bail decisions, annotated across 20+ attributes including bail outcome, IPC sections, crime type, and legal reasoning. Annotations were generated using a prompt-engineered GPT-4o pipeline and verified for consistency. This resource supports a wide range of legal NLP tasks such as outcome prediction, summarization, and fairness analysis, and is the first publicly available dataset focused specifically on Indian bail jurisprudence.

This paper introduces JurisTCU, a Brazilian Portuguese dataset for legal information retrieval (LIR). The dataset is freely available and consists of 16,045 jurisprudential documents from the Brazilian Federal Court of Accounts, along with 150 queries annotated with relevance judgments. It addresses the scarcity of Portuguese-language LIR datasets with query relevance annotations. The queries are organized into three groups: real user keyword-based queries, synthetic keyword-based queries, and synthetic question-based queries. Relevance judgments were produced through a hybrid approach combining LLM-based scoring with expert domain validation. We used JurisTCU in 14 experiments using lexical search (document expansion methods) and semantic search (BERT-based and OpenAI embeddings). We show that the document expansion methods significantly improve the performance of standard BM25 search on this dataset, with improvements exceeding 45% in P@10, R@10, and nDCG@10 metrics when evaluating short keyword-based queries. Among the embedding models, the OpenAI models produced the best results, with improvements of approximately 70% in P@10, R@10, and nDCG@10 metrics for short keyword-based queries, suggesting that these dense embeddings capture semantic relationships in this domain, surpassing the reliance on lexical terms. Besides offering a dataset for the Portuguese-language IR research community, suitable for evaluating search systems, the results also contribute to enhancing a search system highly relevant to Brazilian citizens.

The Brazilian judiciary has a large workload, resulting in a long time to finish legal proceedings. Brazilian National Council of Justice has established in Resolution 469/2022 formal guidance for document and process digitalization opening up the possibility of using automatic techniques to help with everyday tasks in the legal field, particularly in a large number of texts yielded on the routine of law procedures. Notably, Artificial Intelligence (AI) techniques allow for processing and extracting useful information from textual data, potentially speeding up the process. However, datasets from the legal domain required by several AI techniques are scarce and difficult to obtain as they need labels from experts. To address this challenge, this article contributes with four datasets from the legal domain, two with documents and metadata but unlabeled, and another two labeled with a heuristic aiming at its use in textual semantic similarity tasks. Also, to evaluate the effectiveness of the proposed heuristic label process, this article presents a small ground truth dataset generated from domain expert annotations. The analysis of ground truth labels highlights that semantic analysis of domain text can be challenging even for domain experts. Also, the comparison between ground truth and heuristic labels shows that heuristic labels are useful.

There is no instruction for a decision-making process. However, important decisions are usually made by analyzing tons of data to find the optimal way to solve a problem. That's where we truly rely on logic and deduction. That's why surgeons dig into anamnesis, or businesses gather key persons to see a bigger picture before making a turn. Relying on AI decision-making can significantly reduce the time spent on research and data gathering.

"Milton's voice was not stifled or choked for making Satan a heroic figure… in fact, in "Areopagitica", the blind poet champions free speech: Give me the liberty to know, to utter and to argue freely according to conscience above all liberties…. Artificial Intelligence may, in the foreseeable future, be an entity that thinks independently enough to enjoy the pleasures of expression the way humanity does. Whether it may have the potential evils of a Satan is to be seen, but its ontology and a legal framework to accommodate it may be in order. Morality aside, it may be reasonably seen that it is a power that can possibly "make a heaven of hell, a hell of heaven" [2] of the world. It may be pointed out that the possibility of "strong artificial intelligence" -- artificial intelligence constructs that are comparable to a human brain, with features like consciousness that are identified with being human,[3] are entirely hypothetical and may remain so.

Gustav Klimt created some of the world's most expensive masterpieces, but around 20% of his artworks have been lost. Among them are the so-called Faculty Paintings: Philosophy, Medicine, and Jurisprudence. The three pieces are believed to have been destroyed in a fire during World War Two. Only black and white photos of the artworks remain. The original paintings may never be seen again, but machine learning has come close to bringing them back to life.

Jurisprudence has always had to face new challenges posed by innovations, socio-economic developments, and changes in the political landscape. Most recently, various aspects of our life are increasingly becoming entangled with artificial intelligence (AI). The legal fraternity requires much better acquaintance with the technical space as the new policies that they will debate will directly influence the products developed by engineers. To understand the parallelism which one can draw between Artificial Intelligence and Law, let's walk through a few autonomous systems where AI is already confronting the legal field. Constant advancements across a spectrum of technologies brought autonomous cars to reality straight out of sci-fi movies.

The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to begin the discourse on what such an implementation would actually look like with a criticism of pre-processing methods in a legal context . We outline how pre-processing is used to correct biased data and then examine the legal implications of effectively changing cases in order to achieve a fairer outcome including the black box problem and the slow encroachment on legal precedent. Finally we present recommendations on how to avoid the pitfalls of pre-processed data with methods that either modify the classifier or correct the output in the final step.