⚖️ ROSS loses · 🧪 12 checkers tested · 🧠 KM benched

Your AI can draft the memo; you still check its homework. This week: California's 2027 safeguards, hidden instructions in a court filing, and practical ways to make those subscriptions earn their keep. Grab a coffee ☕ and keep your red pen handy.

⚖️ You still own the answer

Courts and lawmakers are putting human responsibility around AI-assisted work. Check the evidence, explain the workflow and distinguish a drafting aid from inputs or methods that need scrutiny.

⚖️ California puts legal AI on a 2027 clock

Put January 1, 2027 in your filing-workflow diary. California's newly chaptered SB574 is scheduled to take effect then, with safeguards against delegating legal practice to generative AI, obligations to check and correct output, and court disclosure. Its confidentiality provision turns on access to sensitive inputs, not a blanket ban on protected systems. Read the enacted text and plan the review steps now; these are California duties scheduled for next year.

Source: California Legislative Information

🏛️ This Illinois judge shows her AI homework

Transparency should work both ways in the courtroom. In a new interview, Madison County judge Sarah Smith explains her August AI addendum: help with organising and drafting is permitted, but judicial decisions stay human and citations get checked. The useful lesson is to spell out the limits of the workflow, rather than wave an 'AI approved' badge. This is one Illinois judge's practice, not a new statewide disclosure requirement.

Source: National Law Review / AI & the Law Newsletter

🕵️ Hidden text? Check what your AI is reading

Your AI intake pipeline can receive instructions you never meant to give it. Tom Martin, LawDroid's CEO, uses a Connecticut filing with hidden white text to argue for invisible-text checks and human escalation before documents reach a model. The underlying August sanction removed the litigant's e-filing privileges; this week's column is fresh practice analysis. It does not show that a judge relied on AI or that the attack succeeded.

Source: Thomson Reuters Institute

🧾 Your expert's AI chat could become evidence

Your expert's AI chat may be evidence, not just a drafting aid. Sidley's new analysis recommends agreeing permitted uses and retention before the engagement, checking citations and ensuring the expert can defend the method. Its older U.S. cases distinguish AI used to filter source material from some protected litigation testing by counsel. These are fact-specific decisions and engagement recommendations, not a universal prompt-discovery rule. Routing work through counsel alone does not guarantee protection.

Source: Sidley Austin / The Legal Intelligencer

🔎 Good answers need receipts

Useful legal AI starts with inputs you can trace and outputs you can inspect. This week brings two research tests, a public-records experiment and a court reminder that source ownership matters.

🧭 Can your AI Act checker show its homework?

Your compliance report needs evidence behind the risk label. This newly submitted preprint studies 12 EU AI Act checkers inspected in September 2025, including 48 reports from eight questionnaire tools across six scenarios. It found uneven legal coverage and recommendations that often lacked concrete next steps. That's a historical sample, not a verdict on today's versions. The authors recommend using checkers for orientation; the study does not certify compliance or establish definitive legal ground truth.

Source: arXiv

🔎 69% on a clause test: keep your red pen

Readable reasons do not make a contract model reliably right. LAURA's 250M-parameter Flan-T5 model scored 69% accuracy and 0.70 F1 on one small English clause-ambiguity dataset. Its explanation-quality comparison used correctly predicted clauses; wrong classifications can still bring bad rationales. The under-review paper tests labels plus explanations within a narrow benchmark. Clause-only context limits cross-reference and wider document analysis, and local inference does not guarantee confidentiality.

Source: arXiv

📚 What can AI find in 4,000 discipline records?

AI's useful legal trick may be reading the pile, not writing another memo. David Stasior describes turning roughly 4,000 Massachusetts attorney-discipline documents spanning 24 years into structured findings, rule references and sanctions. The method opens a route to testing assumptions against public records, while patchy access still gets in the way. This first instalment reports how the dataset was built. It has no released outcome finding yet; rule counts alone cannot establish fairness.

Source: JURIST

📖 ROSS hits a copyright wall over AI training

Training data is a legal design choice, not just a scraping problem. The Third Circuit affirmed partial summary judgment against ROSS over Westlaw's editorial headnotes and rejected fair use on these facts. The opinion distinguishes public judicial text from the publisher's added material. It also describes a non-generative search system. For your legal-AI stack, provenance matters; this interlocutory appeal does not settle every lawsuit over generative-model training.

Source: U.S. Court of Appeals for the Third Circuit

🛠️ Better workflows, fewer expensive guesses

A subscription does not decide who owns the knowledge, checks the work or controls boardroom use. Start with the task and the data path, then ask whether the spend improves the work.

🧠 Bought the AI? Don't bench your knowledge team

Your firm's knowledge does not arrive with the next AI licence. Ryan McClead says firms he knows are moving KM and innovation teams under IT-led AI groups, just as agents need the context those teams maintain. His argument: access to the document system is not the same as knowing the firm's precedents, past mistakes or client preferences. These are client observations and practitioner opinion, explicitly not an industry survey. Question for your next org-chart discussion: who owns that context?

Source: 3 Geeks / The Geek in Review

💸 Renewing AI seats? Check the workflow first

Before you renew another set of AI seats, inspect the work people actually do. In this interview, consultant Maryam Salehijam argues for using existing systems where they fit, then deciding what to buy or build around a specific workflow. Intake and document-check examples make the advice concrete; utilisation and employee satisfaction sit beside time and money in her ROI discussion. These are a commercial guest's recommendations and anecdotes, not audited savings or legal guidance.

Source: 3 Geeks / The Geek in Review

🧪 Small-firm AI: start with one real problem

Your small firm's AI pilot needs a job description before it needs a subscription. A fresh Queensland Law Society panel advised practitioners to pick a narrow problem, compare a few tools, pilot them and check continuing support. Experiment without client information and talk to staff about how AI is already being used. The report gives a practical adoption sequence, with QLS policies and court protocols as follow-through. This is panel advice from Queensland, not a new binding AI rule.

Source: QLS Proctor

📱 Approved AI won't open? Your board needs Plan B

Your board's AI policy needs a plan for the moment the approved tool fails on a phone. Foley's fictional director pastes acquisition details into a personal chatbot, then brings an unchecked answer into the boardroom. The proposed controls cover approved accounts, material reliance, counsel involvement and records under a hold. This is practitioner guidance using a Delaware example, not a reported incident. Neither an approved platform nor a confident answer resolves privilege or disclosure questions.

Source: Foley & Lardner

Community Spotlight

⏱️ Count the checking before you count the savings

Saving 30 minutes sounds great until you spend 20 checking the result. A poster at a six-attorney insurance-defense firm uses that example to ask the question demos skip: what is left after review? The firm has tried file orientation, claim chronologies and first passes through documents, but the poster wants uses that still pay off after citations and flattened facts are checked. Their concern is the time spent across the whole workflow.

Several replies favor chronologies and indices that point back to dated records; one suggests Bates numbers to make checking more targeted. Another describes comparing an export with clean source material before a filing or carrier update leaves the desk. One commenter proposes timing five closed files, recording minutes saved and minutes spent checking separately; that experiment has not been reported as completed. There is also a billing argument: two commenters question whether faster work helps an hourly practice. These practitioner accounts offer a pilot plan, without establishing actual savings or what may be billed.

Source: r/LawFirm

Key Takeaways:

• One commenter proposes timing five closed files, logging minutes saved and minutes spent checking or correcting separately.
• Several replies favor chronologies and document indices tied to dated source records, so the lawyer can verify the output where the evidence sits.
• A commenter suggests citing specific Bates numbers to make review more targeted.
• One commenter describes comparing the AI export with clean source material before a filing or carrier update leaves the desk.
• Two commenters question how faster work affects hourly billables; the thread does not establish what is permissible to bill.