[{"id":3241473,"description":"CALL member Erica Friesen, Research and Instruction Librarian & Online Learning Specialist at the Lederman Law Library at Queen's University in Kingston, Ontario recently wrote an article on \" Thoughts on the New Lexis+ Brief Analysis Tool: For Law Students and Novice Researchers \". It originally appeared on Slaw.ca on September 28, 2022. It is republished here with permission of the author. New academic year; new legal research tools. Something new always comes out right as another cohort of students is gearing up to begin their law degree. And, as with many new product launches these days, “artificial intelligence” is often a prominently displayed term with accompanying materials. As legal publishers continue to launch AI-driven research tools in Canada, what do students and other novice researchers need to know to be prepared for their first forays into legal research? Lexis recently launched the latest version of their legal research platform, Lexis+ Canada, for Canadian law schools. It features significant updates that employ artificial intelligence, including a “Brief Analysis” tool in line with similar products offered by Westlaw Edge (on the US platform) and vLex (already available in Canada). According to Lexis+, this tool helps researchers “expedite completion of briefs and other legal documents.” Once your document is uploaded, it spits out a report including the following categories: 1) recommended cases, 2) cases and legislation cited in your document, 3) jurisdiction, and 4) extracted and recommended concepts. While I do not intend to comment on the utility of this tool for practitioners, I have no doubt that students beginning law school or working in summer or articling positions are bound to encounter this new feature sooner or later. When you do, you may have questions: should I be using this tool? How or how not? The landing page for this tool in Lexis+ contains no information on how this tool works and the Lexis+ Canada “Help” database so far contains minimal documentation. Confusion or uncertainty is understandable with such opacity. To gain some insight, let’s run an experiment: take two case comments written by different authors on the same Supreme Court case from 2004. [1] An older decision means that we should retrieve an abundance of recommendations from the intervening 18 years. Both comments include an overview of the area of law at the time of this decision as well as an analysis of how this particular decision would change the area of law. Once Lexis analyzed these documents, I exported the resulting report and compared the results for each comment. Here are the two documents, in case you’d like to run the experiment yourself: Comment A: Teresa Scassa, “Recalibrating Copyright Law?: A Comment on the Supreme Court of Canada’s Decision in CCH Canadian Limited et al. v. Law Society of Upper Canada ”, Case Comment, (2004) 3:2 CJLT 89. Comment B: WL Hayhurst, “The Canadian Supreme Court on Copyright: CCH Canadian Ltd. v. Law Society of Upper Canada ”, Case Comment, (2004-2005) 41 Can Bus LJ 134. Recommended Cases “Recommendations” is the section of the Brief Analysis report that lists additional case law for you to consider adding to your legal document. Brief Analysis recommended 58 unique cases in total between Comment A and Comment B. However, only 10 of those cases were the same between the two documents (after adjusting for “recommended” cases that were actually cited in the other document). See Figure 1 . Key takeaway: Legal research and writing is an art, not a science. AI-driven tools like these run on the material that you upload, so you can expect substantially different results depending on the quality of analysis and the decisions you made in producing the original document. This does not mean that there is no value in using the tool, but you cannot expect it to help you build something from nothing. Recommendations are based on the content in the document,","source":"Canadian Association of Law Libraries / L'Association canadienne des bibliothèques de droit CALL/ACBD Blog","pubDate":"Mon, 26 Oct 2026 07:55:49 +0100","url":"https://www.callacbd.ca/CALL-Blog/12957419","titre":"CALL Member Article: Thoughts on the New Lexis+ Brief Analysis Tool: For Law Students and Novice Researchers"},{"url":"https://verfassungsblog.de/the-currently-uneven-intelligence-of-ai-in-comparative-constitutional-law/","titre":"The (Currently) Uneven Intelligence of AI in Comparative Constitutional Law","pubDate":"Thu, 15 Oct 2026 08:26:22 +0200","source":"Scoop.it - Documentation juridique (Stéphan Cottin)","description":"This article belongs to the debate » Thinking Machines, Thinking Lawyers14 October 2026The (Currently) Uneven Intelligence of AI in Comparative Constitutional Law From Years of Research to a Single Prompt? Artificial intelligence (AI) has the potential to accelerate and improve the quality of research. This potential has inspired numerous projects exploring how AI could help scholarship on constitutional law. For example, there is research using AI to interpret constitutions,1) apply constitutional principles,2) and predict court rulings.3) Even the question of whether AI could improve the process of constitution-making has been studied.4) AI’s potential is particularly significant for comparative constitutional research. Systematically comparing legal systems requires locating sources from different jurisdictions, which are often difficult to access and only available in local languages. The creation of comparative legal datasets has thus involved considerable personnel, time, and financial resources. AI can overcome many of these obstacles. AI models can search for and translate foreign-language sources, filter them, and convert the filtered information into a dataset. In doing so, AI offers the prospect of datasets — which previously required years of research and the efforts of numerous experts and research assistants to compile — being just a prompt away. However, whether AI models can generate comparative law datasets that are qualitatively comparable to human-coded datasets has not yet been addressed (although Guha et al provide an example for a broader legal benchmarking exercise).5) We therefore conducted our own benchmarking study.6) We found evidence suggesting that recent AI models may be able to code simple factual data, but they are likely unreliable substitutes for expert human coders when conceptual or interpretive judgment is required. The Benchmarking Project: Seven Coding Tasks, Four Datasets, Two LLMs In our project, we tested the ability of two large language models (LLMs) to code different types of information relevant to comparative constitutional law. We specifically assessed whether LLMs can reproduce information from four existing comparative constitutional law datasets, all of which were compiled through human coding and which served as a ‘ground truth’ for our exercise. From these datasets, we asked the LLMs to code seven variables, which can be thought of as seven tasks relevant to the creation of comparative constitutional law datasets. Each of these tasks is either a test of fact-finding or reasoning abilities. First, from the Executive Term Limit Evasion Dataset,7) we drew information on whether presidents in 106 presidential systems have evaded constitutionally mandated term limits between 2000 and 2019. The dataset includes one variable on whether presidents attempt to overstay past the end of their term. The second variable captures what strategies executives use for doing so, such as amending the constitution, replacing it with a new one, or asking the court to interpret term limits out of the constitution. While determining whether a president overstayed is a fact-finding task, assessing the strategy used to do so requires reasoning. Second, from the Comparative Constitution Making Dataset,8) we drew information on the process by which constitutions were adopted in democracies between 1900 and 2015. This dataset also includes fact-finding variables such as (1) whether citizens were involved in the formulation, discussion, or submission of reform proposals, and (2) whether citizens participated in popular referendums at any stage in the process. The dataset’s most important variable (3) captures whether constitutions reflect ‘plural elite agreement,’ that is, whether all relevant elite factions in society were involved and represented during the process. The recreation of this variable requires reasoning. Third, from the Characteristics of National Constitutions Dataset9), we drew on its coding of 43 first-generation constitutional","id":3218421},{"description":"FTC wants your feedback about proposed Made in USA Rule lfair June 22, 2020 | 4:04PM FTC wants your feedback about proposed Made in USA Rule By Lesley Fair If your clients are interested in Made in USA issues – and you know they are – there are two developments at the FTC they need to know about. First, the FTC just announced a Notice of Proposed Rulemaking for a Made in USA Labeling Rule . You’ll want to read the notice in detail, but the proposed Rule would apply to labels on products that make unqualified Made in USA claims. (In FTC parlance, an “unqualified” claim is a broad representation made without limitations.) The proposed Rule would incorporate established guidance already set forth in FTC decisions and orders and in the agency’s 1997 Enforcement Policy Statement on U.S. Origin Claims . Consistent with that guidance, the proposed Rule would prohibit marketers from making unqualified MUSA claims on labels unless: Final assembly or processing of the product occurs in the United States; All significant processing that goes into the product occurs in the United States; and All or virtually all ingredients or components of the product are made and sourced in the United States. The proposed Rule wouldn’t change or affect any other existing federal or state law or regulation relating to country-of-origin labels, but it suggests one notable modification: The proposed Rule would allow the FTC to seek civil penalties for violations. Once the proposed Rule is published in the Federal Register, we invite you to file a public comment either online or on paper. (Thanks for following the instructions for submitting comments included in the notice of proposed rulemaking.) Development #2 is that the FTC just issued a staff report on last fall’s Made in USA workshop convened to consider consumer perception of Made in USA claims and to discuss the agency’s Made in USA enforcement program. Dive into the report for the details, but according to the one panelist who provided survey evidence on how consumers understand Made in USA claims, a 2013 study suggests that almost 3 in 5 Americans agree that “Made in America” means that all parts of a product, including any natural resources it contains, originated in the United States. According to that same study, one third of consumers believe that 100% of a product must originate in a country for it to be called “made” in that country. The report cites those statistics in support of long-standing guidance in the FTC’s Enforcement Policy Statement on U.S. Origin Claims that at least a significant minority of consumers are likely to be deceived by an unqualified Made in USA claim for a product incorporating more than a trivial amount of foreign content.","id":3197412,"source":"FTC USA Business Blog Posts","url":"https://www.ftc.gov/business-guidance/blog/2020/06/ftc-wants-your-feedback-about-proposed-made-usa-rule","titre":"FTC wants your feedback about proposed Made in USA Rule","pubDate":"Mon, 05 Oct 2026 08:07:08 +0200"},{"source":"FTC USA Business Blog Posts","description":"New FTC warning letters cite unsupported Coronavirus-related health and earnings claims sgressin April 24, 2020 | 12:44PM New FTC warning letters cite unsupported Coronavirus-related health and earnings claims By Seena Gressin The FTC is not the pen pal you want if you operate a multi-level marketing company but aren’t closely monitoring your distributors. In its latest round of warning letters, the FTC warns ten MLM companies that they are responsible for the claims made by their participants. The letters direct the companies to report within 48 hours what actions they’ve taken to stop their distributors from claiming their products can treat or prevent Coronavirus disease, that MLM business opportunity participants are likely to earn substantial income, or both. The FTC says that the health and earnings claims are unsubstantiated and therefore violate truth-in-advertising laws. The products at issue are not scientifically proven to treat or prevent COVID-19, the disease that Coronavirus causes. The FTC previously has sent warning letters to companies about their Coronavirus-related health claims, but the new letters are the first to also target claims about potential business opportunity earnings. The letters come at a time when millions of people are out of work and facing financial challenges as a result of the pandemic. MLM companies rely on a network of independent distributors, sometimes called business opportunity participants, to buy and resell their products, often through direct person-to-person sales or online. MLMs typically offer participants a cut of the sales made by people the participant recruits to invest in the business opportunity. The new recruits then try to sell the products, and try to recruit more people to do the same. Most people who join MLMs make little or no money. According to the FTC, distributors of the MLM companies receiving the letters have posted promotional messages on Facebook, Instagram, and other social media sites. Health claims the FTC letters cite include: “STAY SAFE!! MAKE YOUR IMMUNE SYSTEM STRONG!!!” Standard coronavirus protection kit . #antiseptics and #immunity_support.” “HOW WILL YOU FIGHT OFF CORONA? USE NUTRABURST-CHAGA- .... #coronacure #coronaprevention ... #immunesupport #immunebooster ... #immunesystembooster” “Got the coronavirus heebeegeebees? Boost your immunity with this amazing deal!!!!” “... If interested to learn more or obtain oils or rollers Let me know A little extra protection can help #doterra #NursesCOVID19 #Dialysis #ImmunityBoosters #ImproveRespiratoryFunction” Earnings claims the FTC letters cite include: “...Living in quarantine and where 14 million people applied for unemployment just last week ... I’ll stick with the opportunity to change people’s lives ... turn a small investment into six figures .... #arbonne ... #quarantine #2020” “Need to make extra money? Find it difficult to pay your bills? Were you laid off/ #fired? Be your own Boss w/doTERRA essential oils. Msg me to achieve financial independence #laidoff #unemployed #cantpaymybills #cantpaymyrent #student #sales #sidehustle #makemoney #stayathomemom.” “[E]veryone’s getting stimulus checks right now... There is no better investment you could do... Take that money that you’re about to get back... figure out a way to make this happen tonight.” In the letters alleging unsubstantiated health claims, FTC staff reminds the companies that, under the FTC Act, claims that a product can prevent, treat, or cure a serious disease require the support of well-controlled human clinical studies. Because “no such study is currently known to exist” for the advertised products, the FTC says the companies “must immediately cease making all such claims.” In the letters alleging deceptive earnings claims, FTC staff reminds the companies that under the FTC Act, “claims about the potential to achieve a wealthy lifestyle, career-level income, or significant","id":3197413,"url":"https://www.ftc.gov/business-guidance/blog/2020/04/new-ftc-warning-letters-cite-unsupported-coronavirus-related-health-earnings-claims","titre":"New FTC warning letters cite unsupported Coronavirus-related health and earnings claims","pubDate":"Mon, 05 Oct 2026 08:07:08 +0200"},{"titre":"Shadow AI: A Growing Problem for Canadian Courts","url":"https://www.slaw.ca/2026/08/26/shadow-ai-a-growing-problem-for-canadian-courts/","pubDate":"Thu, 27 Aug 2026 07:57:55 +0200","description":"Many generative AI tools are relatively easy to access and use. For example, no technical expertise is required to pull up and type a prompt into ChatGPT, and the most basic version is free. This reality has led to a significant amount of “shadow AI” use within organizations; that is, uses of AI that are not formally approved or are even contrary to formal policies. Concerns about shadow AI commonly arise in the context of private companies, with some studies indicating that over 80% of workers report using unapproved tools at work. Much less discussed – and worthy of far . . . [more] The post Shadow AI: A Growing Problem for Canadian Courts appeared first on Slaw","id":3118317,"source":"Slaw"},{"url":"https://www.arthurperret.fr/blog/2023-11-01-document-theory-and-document-design.html","titre":"[Article] Document theory and document design","pubDate":"Thu, 23 Jul 2026 10:17:34 +0200","description":"This paper reports on an experiment which connects document design with document theory. Document design is about what documents should do. We use the example of document graphs and of Cosma, a visualization program created during the HyperOtlet research programme, to show how theory can inform design decisions and help conceptualize results. This paper is currently under peer review.