An artist has said he is taking legal advice after accusing the Conservative Party of copying his cartoon image of Andy Burnham at their party conference. People attending the party's conference in Birmingham have been handed water bottles featuring the image and labelled Bottler Burnham, 0% strength. Labour supporter Stanley Chow, who created the portrait for Burnham's 2021 mayoral campaign in Greater Manchester, has claimed the party did not ask for permission to use the image. A Conservative Party spokeswoman said the bottles were were not on sale and merely handed out to attending media in goody bags. Manchester illustrator Chow said: I was stunned and angry when I saw what they had done.

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Legal consultation is essential for safeguarding individual rights and ensuring access to justice, yet remains costly and inaccessible to many individuals due to the shortage of professionals. While recent advances in Large Language Models (LLMs) offer a promising path toward scalable, low-cost legal assistance, current systems fall short in handling the interactive and knowledge-intensive nature of real-world consultations. To address these challenges, we introduce LeCoDe, a real-world multi-turn benchmark dataset comprising 3,696 legal consultation dialogues with 110,008 dialogue turns, designed to evaluate and improve LLMs' legal consultation capability. With LeCoDe, we innovatively collect live-streamed consultations from short-video platforms, providing authentic multi-turn legal consultation dialogues. The rigorous annotation by legal experts further enhances the dataset with professional insights and expertise. Furthermore, we propose a comprehensive evaluation framework that assesses LLMs' consultation capabilities in terms of (1) clarification capability and (2) professional advice quality. This unified framework incorporates 12 metrics across two dimensions. Through extensive experiments on various general and domain-specific LLMs, our results reveal significant challenges in this task, with even state-of-the-art models like GPT-4 achieving only 39.8% recall for clarification and 59% overall score for advice quality, highlighting the complexity of professional consultation scenarios. Based on these findings, we further explore several strategies to enhance LLMs' legal consultation abilities. Our benchmark contributes to advancing research in legal domain dialogue systems, particularly in simulating more real-world user-expert interactions.

The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that omit critical information. This deficiency makes it challenging for traditional legal question-answering systems to accurately identify users' actual needs, often resulting in imprecise or generalized advice. In this work, we develop a legal question-answering system called Intelligent Legal Assistant, which interacts with users to precisely capture their needs. When a user poses a question, the system requests that the user select their geographical location to pinpoint the applicable laws. It then generates clarifying questions and options based on the key information missing from the user's initial question. This allows the user to select and provide the necessary details. Once all necessary information is provided, the system produces an in-depth legal analysis encompassing three aspects: overall conclusion, jurisprudential analysis, and resolution suggestions.

The rapid proliferation of large language models (LLMs) as general purpose chatbots available to the public raises hopes around expanding access to professional guidance in law, medicine, and finance, while triggering concerns about public reliance on LLMs for high-stakes circumstances. Prior research has speculated on high-level ethical considerations but lacks concrete criteria determining when and why LLM chatbots should or should not provide professional assistance. Through examining the legal domain, we contribute a structured expert analysis to uncover nuanced policy considerations around using LLMs for professional advice, using methods inspired by case-based reasoning. We convened workshops with 20 legal experts and elicited dimensions on appropriate AI assistance for sample user queries (``cases''). We categorized our expert dimensions into: (1) user attributes, (2) query characteristics, (3) AI capabilities, and (4) impacts. Beyond known issues like hallucinations, experts revealed novel legal problems, including that users' conversations with LLMs are not protected by attorney-client confidentiality or bound to professional ethics that guard against conflicted counsel or poor quality advice. This accountability deficit led participants to advocate for AI systems to help users polish their legal questions and relevant facts, rather than recommend specific actions. More generally, we highlight the potential of case-based expert deliberation as a method of responsibly translating professional integrity and domain knowledge into design requirements to inform appropriate AI behavior when generating advice in professional domains.

Chatbots have been around since the 1960s and coders have been trying to pass the Turing test ever since, creating increasingly sophisticated iterations of natural language processing (NLP) software. A recent episode, where a Google engineer was sacked for claiming that the search engine's chatbot generator software known as LaMDA was sentient, perhaps demonstrates the leaps and bounds that NLP has made over the past few years. However, it's only with the public release of a new chatbot called ChatGPT that the potential of NLP has been taken seriously by the wider public. ChatGPT is a conversational piece of software released by OpenAI, designed to answer questions posed in natural language and even have a dialogue with users. It has been trained on a multitude of online data from Wikipedia to Reddit, although the information is only correct up until 2021. As well as answering general queries and therefore being a potential threat to Google, it also has the ability to write bespoke articles on any topic which is sparking off existential debates amongst academics and professional writers.

An artificial intelligence developed by DoNotPay is expected to advise a defendant in court this February in possibly the first-ever case argued by an AI, Metro reported on Friday. The AI will provide legal advice to a defendant on trial for a speeding ticket via an earpiece, according to the New Scientist. DoNotPay CEO Joshua Browder pledged to recompensate the defendant for any fines that could be incurred if the case is lost. Browder initially launched the company in 2015 as a chatbot that provides legal advice to people facing fines or late fees, according to the Metro report. Browder said that there are liability risks and that he is training the AI on case law and making sure it remains honest, according to NDTV.

Research and development in new technologies for migration management are rapidly increasing. To quote certain migration examples, big data was used to predict population movements in the Mediterranean, AI lie detectors used at the European border, and the recent one is the government of Canada using automated decision-making in immigration and refugee applications. Artificial intelligence in migration is helping countries to manage international migration. Every corner of the world is encountering an unprecedented number of challenging migration crises. As an increasing number of people are interacting with immigration and refugee determination systems, nations are taking a stab at artificial intelligence. AI in global immigration is helping countries to automate a plethora of decisions that are made almost daily as people want to cross borders and look for new homes.

An academic and a lawyer have teamed up to develop a robot lawyer, which, if successful, will make legal advice affordable to people from all backgrounds, while revolutionising the legal sector. Robots could take on significant parts of a lawyer's work, reducing the costs and barriers to access to legal services for everyone, rather than just those who can afford the high costs. The project, at the University of Bradford, is initially working on a machine learning-based application to provide immigration-related legal advice, but if successful, it could be replicated across the legal sector. The idea has received government backing in the form of a £170,000 grant from Innovate UK Knowledge Transfer Partnerships. Legal firm AY&J Solicitors is providing a further £70,000 as well as the vital knowledge of lawyers.

Advances in Artificial Intelligence (AI) and Machine Learning (ML) that are being applied to legal efforts have raised controversial questions about the existent restrictions imposed on the practice-of-law. Generally, the legal field has sought to define Authorized Practices of Law (APL) versus Unauthorized Practices of Law (UPL), though the boundaries are at times amorphous and some contend capricious and self-serving, rather than being devised holistically for the benefit of society all told. A missing ingredient in these arguments is the realization that impending legal profession disruptions due to AI can be more robustly discerned by examining the matter through the lens of a framework utilizing the autonomous levels of AI Legal Reasoning (AILR). This paper explores a newly derived instrumental grid depicting the key characteristics underlying APL and UPL as they apply to the AILR autonomous levels and offers key insights for the furtherance of these crucial practice-of-law debates.