The rapid advancement of large language models (LLMs) has significantly propelled progress in natural language processing (NLP). However, their effectiveness in specialized, low-resource domains-such as Arabic legal contexts-remains limited. This paper introduces MizanQA (pronounced Mizan, meaning "scale" in Arabic, a universal symbol of justice), a benchmark designed to evaluate LLMs on Moroccan legal question answering (QA) tasks, characterised by rich linguistic and legal complexity. The dataset draws on Modern Standard Arabic, Islamic Maliki jurisprudence, Moroccan customary law, and French legal influences. Comprising over 1,700 multiple-choice questions, including multi-answer formats, MizanQA captures the nuances of authentic legal reasoning. Benchmarking experiments with multilingual and Arabic-focused LLMs reveal substantial performance gaps, highlighting the need for tailored evaluation metrics and culturally grounded, domain-specific LLM development.

The persistent accumulation of unresolved legal cases, especially within the Indian judiciary, significantly hampers the timely delivery of justice. Manual methods of prioritizing petitions are often prone to inefficiencies and subjective biases further exacerbating delays. To address this issue, we propose LLMPR (Large Language Model-based Petition Ranking), an automated framework that utilizes transfer learning and machine learning to assign priority rankings to legal petitions based on their contextual urgency. Leveraging the ILDC dataset comprising 7,593 annotated petitions, we process unstructured legal text and extract features through various embedding techniques, including DistilBERT, LegalBERT, and MiniLM. These textual embeddings are combined with quantitative indicators such as gap days, rank scores, and word counts to train multiple machine learning models, including Random Forest, Decision Tree, XGBoost, LightGBM, and CatBoost. Our experiments demonstrate that Random Forest and Decision Tree models yield superior performance, with accuracy exceeding 99% and a Spearman rank correlation of 0.99. Notably, models using only numerical features achieve nearly optimal ranking results (R2 = 0.988, \r{ho} = 0.998), while LLM-based embeddings offer only marginal gains. These findings suggest that automated petition ranking can effectively streamline judicial workflows, reduce case backlog, and improve fairness in legal prioritization.

A New York lawyer could face discipline after it was discovered a case she cited was generated by artificial intelligence and did not actually exist. The 2nd U.S. Circuit Court of Appeals ordered lawyer Jae Lee to its grievance panel last week after discovering she used OpenAI's ChatGPT to research prior cases for a medical malpractice lawsuit but failed to confirm whether the case she was citing actually existed, according to a report from Reuters. The attorney included the fictitious state court decision in an appeal for her client's lawsuit claiming that a Queens doctor botched an abortion, according to the report, leading the court to order that Lee submit a copy of the decision that the lawyer later found she was "unable to furnish." The lawyer's conduct "falls well below the basic obligations of counsel," the 2nd U.S. Circuit Court of Appeals concluded in its disciplinary review, which was sent to Lee. Lee would later admit to using a case that was "suggested" to her by ChatGPT, a popular AI chatbot, and failing to verify the results herself. The lawyer's decision to use the popular application comes even though experts have warned against such practices, noting that AI is a relatively new technology that also is well-known for "hallucinating" false or misleading results.

In the not too distant past, many were convinced that Artificial Intelligence (AI) or Machine Learning (ML) would not substantially change the practice of law. The legal profession was considered to be -- by its very nature -- requiring specialist skills and nuanced judgment that only humans could provide and would therefore be immune to the disruptive changes brought about by the digital transformation. However, the application of ML technology in the legal sector is now increasingly mainstream, particularly as a tool to save time for lawyers and provide a richer analysis of ever-larger datasets to aid legal decision-making in judicial systems throughout the world. One key area of ML application in judicial systems is in "predictive justice". This involves using ML algorithms that perform a probabilistic analysis of any given particular legal dispute using case law precedents.

'Tucker Carlson Tonight' host makes the case for why Kyle Rittenhouse is not receiving a fair trial The judge in the Kyle Rittenhouse trial has just sent the jurors home for the night to think about the trial for yet another day. So far, deliberations, in this case, have lasted about 20 hours. In a normal proceeding, we'd have the jury's decision in about 20 minutes. The essential question, in this case, is really clear did Kyle Rittenhouse have good reason to believe dangerous men were trying to murder him? And the answer is also clear and unequivocal?

When an Uber autonomous test car killed pedestrian Elaine Herzberg in Tempe, Arizona, in March 2018, it sent alarm bells around the world of artificial intelligence (AI) and machine learning. Walking her bicycle, Herzberg had strayed on to the road, resulting in a fatal collision with the vehicle. While there were other contributory factors in the accident, the incident highlighted a key flaw in the algorithm powering the car. It was not trained to cope with jay-walkers nor could it recognise whether it was dealing with a bicycle or a pedestrian. Confused, it ultimately failed to default quickly to the safety option of slowing the vehicle and potentially saving Herzberg's life.

Currently, the number of judges in Estonia remains the same as twenty years ago. While, the number of cases registered in Estonian courts has increased as doubled over that time span. Given the complications of the judicial system from the local to the European Union level, the burden on the court system appears unlikely to diminish– admittedly, the opposite appears much more probable. It indicates that this is the ideal time for AI companies to develop systems that help judicial experts to give less time on time-consuming tasks and find judgments to supersede with automated systems. Applications of artificial intelligence can predict the outcomes of processes and identify new patterns.

Technologies like AI, blockchain, cognitive computing, and advanced data analytics will be a boon for the judicial system in many ways. Countries are even thinking about robot judges.

In this writing, readers will get to know in what way AI might replace the key procedures in the judicial system around the world. Well, if you wish to discover the role of AI in the judicial system and check a few quite controversial but innovative opinions on the above-mentioned subjects, you should start reading this article immediately! The majority of experts in AI development report that in the future AI will become a decent substitution for human jobs. However, should AI fully replace judges and legal officers? Here, we are going to clarify where AI is implemented in the judicial systems of such high-developed countries as the US and China.

Artificial intelligence has become a fundamental piece of everything from medical diagnostics technology to systems that analyze electoral candidates and provide accurate information to voters. However, you may still find many AI skeptics, and especially people who question the role of AI in the justice system. Many legal leaders and institutions are interested in the efficiency benefits AI brings to the field. But the big question is: can AI make the judicial system fairer? Many claim that the United States' judicial system is among the most robust in the world.