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How Police Make Up The Law (ft. LegalEagle)
Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large language models used in these tools are prone to "hallucinate," or make up false information, making their use risky in high-stakes domains. Recently, certain legal research providers have touted methods such as retrieval-augmented generation (RAG) as "eliminating" (Casetext, 2023) or "avoid[ing]" hallucinations (Thomson Reuters, 2023), or guaranteeing "hallucination-free" legal citations (LexisNexis, 2023). Because of the closed nature of these systems, systematically assessing these claims is challenging. In this article, we design and report on the first preregistered empirical evaluation of AI-driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general-purpose chatbots (GPT-4), we find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI-Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. We also document substantial differences between systems in responsiveness and accuracy. Our article makes four key contributions. It is the first to assess and report the performance of RAG-based proprietary legal AI tools. Second, it introduces a comprehensive, preregistered dataset for identifying and understanding vulnerabilities in these systems. Third, it proposes a clear typology for differentiating between hallucinations and accurate legal responses. Last, it provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs, which remains a central open question for the responsible integration of AI into law.
Governing Digital Legal Systems: Insights on Artificial Intelligence and Rules as Code · MIT Computational Law Report
This article explores how AI and 'rules as code' are turning law into automated systems. It highlights the need for governance focused on transparency, explainability, and risk management to ensure these digital legal frameworks stay reliable and fair.
Directing large language models to follow the letter or spirit of the law
The distinction between the spirit and letter of the law is a central issue across research and everyday life, and a growing concern for building safe, intelligent machines. What is this distinction based on, and how can we develop machines that follow the intention behind a rule? We used targeted adaptation that made large language models prioritize the spirit or letter of the law. With minimal modifications, our method significantly changed LLM behavior across diverse measures, novel vignettes, real-world scenarios, and influential legal cases. An analysis of model internals revealed a low-dimensional space with three interpretable dimensions matching a formal pre-specified framework for the geometry of legal concepts. These findings show how legal thought in LLMs may be organized and directed.
Free Legal Help Guides & Resources by PLEO | Law For Non-Profits
Law for Non-Profits offers free self-serve information that help non-profits avoid common legal pitfalls.
AI for Law Firms: What the 8am Legal Industry Report Tells Us About AI Use
New data from 1,300+ legal professionals shows generative AI use has more than doubled year over year. The real question now: How should law firms structure governance and integration?
Steal the Internet - Archiving Everything and Sharing It With Others
2014: 70% of the links within legal journals and 50% of the URLs from Supreme Court decisions did not contain the originally cited material.
Free Legal Help Guides & Resources by PLEO | BC Law For Non-Profits
Law for Non-Profits offers free self-serve information that help non-profits avoid common legal pitfalls.
Robert Diab
A blog about law and technology with a focus on privacy, online harms, and expression
AI Hallucination Cases Database – Damien Charlotin
The most comprehensive database of AI hallucination cases in law: legal decisions from courts worldwide, searchable by country, party, AI tool, and outcome. Updated daily.
Litigation Tracker: Legal Challenges to Trump Administration Actions
A public resource tracking the legal challenges to the Trump administration's executive orders and actions.
Court Filings in A.I. Suit Invoke Copyright Law, Culture and Sports
Filings made Friday in The New York Times’s closely watched lawsuit against OpenAI and Microsoft included a range of copyright law and cultural references.
“The Template for Law Schools Around the World”: UChicago Law School Announces New AI Strategy
The Law School will prohibit electronic devices in most 1L courses next academic year, according to a broad strategy statement outlining its new approach to legal education amid increasing use of AI at universities and in the field.
Wex
Wex is a free legal dictionary and encyclopedia sponsored and hosted by the Legal Information Institute at Cornell Law School. The goal of the Wex project is to use law students to demystify legal language to the best of our collective ability. Nothing in Wex should be construed as legal advice, nor should it be considered to be the position or opinion of Cornell University, Cornell Law School, or the faculty of either. More information about Wex can be found in our Wex Definitions page. Here's a list of all pages.
Editor's Notes: Can We Defy Courts While Believing In Rule of Law?
Do our tactics throw out democracy and rule of law while purportedly aiming at saving them?
