This study discusses the essential components that a Retrieval-Augmented Generation (RAG)-based LLM system should possess in order to support Japanese medical litigation procedures complying with legal norms. In litigation, expert commissioners, such as physicians, architects, accountants, and engineers, provide specialized knowledge to help judges clarify points of dispute. When considering the substitution of these expert roles with a RAG-based LLM system, the constraint of strict adherence to legal norms is imposed. Specifically, three requirements arise: (1) the retrieval module must retrieve appropriate external knowledge relevant to the disputed issues in accordance with the principle prohibiting the use of private knowledge, (2) the responses generated must originate from the context provided by the RAG and remain faithful to that context, and (3) the retrieval module must reference external knowledge with appropriate timestamps corresponding to the issues at hand. This paper discusses the design of a RAG-based LLM system that satisfies these requirements.

The company is pursuing aggressive legal tactics against its opponents. On September 12, Jay Edelson received what he expected to be a standard legal document. Edelson is a lawyer representing the parents of Adam Raine; they are suing OpenAI, alleging that their 16-year-old son took his life at the encouragement of ChatGPT. OpenAI's lawyers had some inquiries for the opposing counsel, which is normal. For instance, they requested information about therapy Raine may have received, and Edelson complied.

This paper examines the role of public interest litigation in promoting accountability for AI and automated decision-making (ADM) in Australia. Since ADM regulation faces geopolitical headwinds, effective governance will have to rely at least in part on the enforcement of existing laws. Drawing on interviews with Australian public interest litigators, technology policy activists, and technology law scholars, the paper positions public interest litigation as part of a larger ecosystem for transparency, accountability and justice with respect to ADM. It builds on one participant's characterisation of litigation about ADM as an exercise in legal retrofitting: adapting old laws to new circumstances. The paper's primary contribution is to aggregate, organise and present original insights on pragmatic strategies and tactics for effective public interest litigation about ADM. Naturally, it also contends with the limits of these strategies, and of the Australian legal system. Where limits are, however, capable of being overcome, the paper presents findings on urgent needs: the enabling institutional arrangements without which effective litigation and accountability will falter. The paper is relevant to law and technology scholars; individuals and groups harmed by ADM; public interest litigators and technology lawyers; civil society and advocacy organisations; and policymakers.

Rise of the'porno-trolls': how one porn platform made millions suing its viewers Instead, it was a subpoena. He had been sued in federal court for illegally downloading 80 movies. Some of the titles sounded cryptic - Do Not Worry, We Are Only Friends - or banal, like International Relations Part 2. Others were less subtle: He Loved My Big Ass, He Loved My Big Butt, and My Big Booty Loves Anal. Brown, who had spent decades investigating sex crimes, claimed he had never watched any of them. His years "dealing with pimping", he wrote in a court filing, left him "with no interest in pornography". He had been married for 40 years, he did not need to download Hot Wife, another title in the list.

Strategic litigation involves bringing a legal case to court with the goal of having a broader impact beyond resolving the case itself: for example, creating precedent which will influence future rulings. In this paper, we explore strategic litigation from the perspective of machine learning theory. We consider an abstract model of a common-law legal system where a lower court decides new cases by applying a decision rule learned from a higher court's past rulings. In this model, we explore the power of a strategic litigator, who strategically brings cases to the higher court to influence the learned decision rule, thereby affecting future cases. We explore questions including: What impact can a strategic litigator have? Which cases should a strategic litigator bring to court? Does it ever make sense for a strategic litigator to bring a case when they are sure the court will rule against them?

A transfer order made by the US judicial panel on multidistrict litigation on Thursday said that centralisation will "allow a single judge to coordinate discovery, streamline pretrial proceedings, and eliminate inconsistent rulings". Cases brought in California by prominent authors including Ta-Nehisi Coates, Michael Chabon, Junot Díaz and the comedian Sarah Silverman will be transferred to New York and joined with cases brought by news outlets, including the New York Times, and other authors including John Grisham, George Saunders, Jonathan Franzen and Jodi Picoult. Most of the plaintiffs opposed consolidation, arguing that their cases were too different to be combined. OpenAI had proposed consolidating the cases in northern California. The judicial panel ultimately transferred the cases to the southern district of New York, stating that centralisation would "serve the convenience of the parties and witnesses" and "promote the just and efficient conduct of this litigation".

On a recent Wednesday night in Los Angeles, I was ready to buy a hot dog with my face. I was at the Intuit Dome, a 2 billion entertainment complex that opened earlier this month. Soon, it will be the home of the L.A. Clippers, but I was there to watch Olivia Rodrigo, queen of teen angst, perform a sold-out show. The arena was filled with people wearing purple cowboy hats and the same silver sequin miniskirt, all of us ready to scream-sing for two hours straight. But first, we needed food.

The proliferation of blockchain entities (persons or enterprises) exposes them to potential regulatory actions (e.g., being litigated) by regulatory authorities. Regulatory frameworks for crypto assets are actively being developed and refined, increasing the likelihood of such actions. The lack of systematic analysis of the factors driving litigation against blockchain entities leaves companies in need of clarity to navigate compliance risks. This absence of insight also deprives investors of the information for informed decision-making. This study focuses on U.S. litigation against blockchain entities, particularly by the U.S. Securities and Exchange Commission (SEC) given its influence on global crypto regulation. Utilizing frontier pretrained language models and large language models, we systematically map all SEC complaints against blockchain companies from 2012 to 2024 to thematic factors conceptualized by our study to delineate the factors driving SEC actions. We quantify the thematic factors and assess their influence on specific legal Acts cited within the complaints on an annual basis, allowing us to discern the regulatory emphasis, patterns and conduct trend analysis.

You wake up on Election Day and unlock your phone to a shaky video of your state capitol. In other clips posted alongside it, gunshots ring out in the distance. You think to yourself: Maybe better to skip the polling booth today. Only later do you learn that the videos were AI forgeries. A friend calls you, distraught.

The lawsuits allege Udio produced output with "striking resemblances" to songs including Dancing Queen by ABBA and All I Want For Christmas Is You by Mariah Carey, while Suno allegedly turned out songs similar to I Got You (I Feel Good) by James Brown and Johnny B. Goode by Chuck Berry, among others. Record labels were able to basically recreate versions of very famous songs with highly specific prompts, then linked to them in the lawsuits. I made a short compilation here:https://t.co/9Nu7rW7eqD These lawsuits are not the first to trouble the booming generative AI industry. Visual artists have sued makers of image generating systems, while various newspapers are suing OpenAI, the owner of ChatGPT, for similar allegations.