Daniel W. Linna Jr. (@DanLinna) header
Daniel W. Linna Jr. (@DanLinna) profile picture

prof & Dir Law & Technology, @NorthwesternLaw & @NorthwesternEng; @CodeXStanford Affiliated Faculty; frmr #BigLaw partner; AI4Law; Law4AI; ppl+process+data+tech

Chicago, IL @NorthwesternU
Joined February 2013
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Law must be a part of computer science curricula Agreed, @mireillemoret! Excellent @CACMmag piece! So much important CS+Law work to be done! We're onboard at @NorthwesternU w interdisciplinary CS+Law courses in both @NorthwesternEng & @NorthwesternLaw! cacm.acm.org/magazines/2021/…
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Scholastica, making buckets of money on AI-aided submissions, decides to push law reviews the least helpful solution: disclosure. And apparently did so without talking to faculty or even students about what they wanted or how to design it.🤷🤷 blog.scholasticahq.com/post/…
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Percentage of federal civil cases reaching trial, 1962-2025
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Update: Can AI agents use Pangram to beat Pangram? In small tests, I found simple prompts to the Codex allowed it to beat Pangram. The final output was 100% human generated. Prompts that helped: - Have the agent develop a style guide based on past writing - Have it develop an "anti-style guide" based on AI writing tics that people have complained about - Instruct it to get to a seed sentence that Pangram doesn’t flag (or at least flags as AI-assisted rather than fully AI-generated). Then the agent knows what strategies work and can iterate from there
I tried exactly this back in February using Claude Code. It worked pretty easily, though ~all of the important ideas had to come from me and CC itself couldn't figure them out.* I tried it again a week ago. Complete failure, despite Claude being much better and not needing as much guidance from me. Pangram seems to have been dramatically hardened in the last few months. The API kept returning scores > 0.99 for AI writing so there wasn't even a gradient for it to figure out what's working and what isn't. * Things it couldn't figure out on its own a few months ago, even after a bunch of iterations: 1) Remove well-known tells like em-dashes (!) 2) Do controlled experiments instead of stuffing a bunch of ideas at once 3) Start from known-best text and surgically make edits. Much better than trying to rewrite the whole essay (or even paragraphs or sentences) because the that just produces text from the LLM distribution all over again. In my more recent experiment, 1) and 2) were obvious to Fable but 3) still wasn't. Pretty surprising. Anyway, hats off to @pangram. I'll probably give it another shot at some point but I'm more-or-less ready to change my mind as an AI-detection skeptic. This was the LLM summary from my first experiment BTW. Still gives me a chuckle.
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Stop the AI scold; focus instead on the substance of what the AI makes
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Artificial intelligence will transform more jobs than it eliminates outright. The biggest productivity gains will come not from incorporating AI into existing workflows but from redesigning those workflows entirely. @MITshapingwork mitsloan.mit.edu/ideas-made-…
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A few years ago, “legal engineer” would have gotten blank stares. Now it’s in @nytimes. Why? AI made it clear that the hard part isn’t buying technology. It’s redesigning the work around it. nytimes.com/2026/10/31/busin…
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The CSLaw Workshop is starting up for 2026-2027. This month, on Friday, Oct 30, 1-3pm Central we will explore parallels in AI use in academic peer review and in federal courts, and aiming to find connections between these two research communities. cslawworkshop.org
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This is an important perspective that in my view is much more useful than running taste tests between human and AI reviews. Human-only review is over. It's all about designing the hybrids correctly.
New paper: Rather than automating review, can we target AI to known weaknesses of human review? Consider lottery variance, where differnet reviewer draws change outcomes. We show how LLM-implemented checklists learned from prior review can reduce variance w/limited added bias 🧵
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New paper: Rather than automating review, can we target AI to known weaknesses of human review? Consider lottery variance, where differnet reviewer draws change outcomes. We show how LLM-implemented checklists learned from prior review can reduce variance w/limited added bias 🧵
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What would the writing of superintelligent AI sound like? You might expect it to have the characteristics of good human writing — figurative language, high information density, coining new terms, and punchiness — but dialed up to 11. Well, that’s what Claude did, until Anthropic dialed it back recently because of user complaints. It didn’t sound superintelligent to most of us. Instead, the neuralese writing style sounds like a “stupid person’s idea of a smart person”. Our response to information density and metaphor-laden writing looks something like the cartoon graph I've drawn. Beyond a point, it completely backfires. The reason human writers don’t overdo it isn’t because they aren’t capable, but because they are aware of the reader’s cognitive burden. One of the hardest skills to learn as a writer is to simulate the reader’s experience. It’s easy to train AI to mimic mediocre human writing. But optimizing it to be (perceived as) good is really hard, and made much harder by the fact that when everyone uses the same few AI models we get tired of their tics very quickly. Most Claudeisms are individually pretty good. I hate that I can’t use “load bearing” in my writing any more. If it weren’t for AI overuse it would be a neat metaphor. A broader question is whether most tasks in the economy are like chess, where there is a clear performance metric that can be optimized, or whether they’re like writing, where quality is a multidimensional balancing act without clear training signals beyond human mimicry.
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Lots of debate this week on whether journals should ban AI in peer review. We actually tested this in our new preprint, and the results were pretty clear to me. For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science. TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵
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Legaltech friends: VC money is NOT the only way to get rich. But, when it works … oh boy! Many of my friends in Legaltech made $5-$30m in exits involving very little (but not $0) outside capital. They then go on to run other companies, and make more. Pretty nice life if you ask me. If you want to be a billionaire (see my pinned post) you gotta take massive outside capital. If you raise big and don’t exit, which happens, it can be pretty painful.
We don’t take VC money but I watch closely (and sometimes enviously) at the fast-growing Legal Techs that do. Here’s the data I look at to determine how the cash is fueling growth (without the benefit of knowing actual ARR).
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For anyone interested in the regulation of #clinicalAI, I'm hosting a webinar next week w/ reps from the Federation of State Medical Boards and the AMA to hear how they think about clinical AI regulation and surrounding policy. Registration link here: wsgr.com/en/events/clinical-…
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Yesterday, @thehoffprof asked how we can manage law review article selection in times of the slopalypse Today, a new study offers some evidence for fighting AI flooding with AI. In head-to-head comparisons, AI-generated reviews beat reviews by paid human experts. AI judges overwhelmingly preferred them. Three former journal editors also preferred them in 26 of 30 judgments. That doesn't mean that AI should select our future scholarship. The downsides are obvious. The study doesn't really test selection decisions, much less questions of scholarly impact. But it makes the case for using AI-assisted triage to direct scarce human attention toward interesting, rigorous, and creative articles Complain about it, by all means. But the flood is coming either way. If you don't have a better AND realistic solution, the complaint won't get us very far.
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By every measure, Opus 4.8 beats human experts as peer reviewers in a study of 124 Harvard social science papers covering a range from statistical analyses to formal models to qualitative work. As academics, we need to figure out how to adapt the practice of science to AI, fast.
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Law folks: If you’re not using Claude Code or ChatGPT’s Codex, you have no idea what you’re missing. So I’m distributing my research tools that provide a huge boost to legal scholarship. Research assistance, cite-checking, proofreading, and more (thread): danepps.github.io/legal-rese…
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🚨CFP: Computer Science & Law 27 🚨 Tomorrow is the last day to submit to CS&Law 2027! We welcome papers at the intersection of these two fields and have both an archival and non-archival (for previously published and working papers)! computersciencelaw.org
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"Frontier AI is itself a firewall" and "slowing its development can postpone new defenses" and "cost protection as well as progress." Excellent @WSJ op-ed by my @NorthwesternLaw colleague, @joldmcginn.
The safest course on AI is not necessarily the slowest. More capable models may be our best defense against malicious uses of AI. A slowdown can cost us protection as well as progress, as I write in the WSJ today: wsj.com/opinion/the-best-def…
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The safest course on AI is not necessarily the slowest. More capable models may be our best defense against malicious uses of AI. A slowdown can cost us protection as well as progress, as I write in the WSJ today: wsj.com/opinion/the-best-def…
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Update: We didn’t start preparing
It won’t be long before AI demonstrably outperforms college teachers in educating students, by a wide margin, and at a fraction of the cost. It would be in our interest to start preparing now for what our value-added will be in this new age.
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