I beat lawyers twice before generative AI even existed.
Recently I asked Gemini a few questions about personal conflicts in everyday life. It's often too conservative, with views too shallow for the problem. So I still handle human conflicts myself. I only outsource the templated stuff like routine chat replies or marketing copy though it saves me huge amount of time. People who quote AI in serious conflicts are too weak to handle them on their own.
AI outperforms law professors in Stanford Law study
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Re: AI outperforms law professors in Stanford Law study
#92As a software engineer I have some intuition for what the risks are of letting agents do some tasks vs others. I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
I'm afraid since claude cheats in benches, what will it do with law?
Cheat.
Or worse, use historical data to determine the laws of today.
Re: AI outperforms law professors in Stanford Law study
#93What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated statement.
Re: AI outperforms law professors in Stanford Law study
#94What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated…
A human has a motive that exists that frames the thought being expressed. An LLM is going to be creating a “de novo” thought in response to a line of questioning.
Re: AI outperforms law professors in Stanford Law study
#95What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated…
Same is probably true of humans. In a conversation, we often respond from instinct, then work backwards to a rationalization only when asked. For more considered thoughts, if we’re lucky, we can remember our “reasoning traces” but that’s as deep as our introspection goes. Unless we’re neuroscientists, we don’t even know how many neurons we have, let alone have any understanding of how they generate our thoughts. Motivated reasoning impairs our introspection further, and then dishonesty and communication errors prevent us from relaying the limited remaining information to each other.
Model interpretability work has advanced a lot. Arguably we already can explain AI decision-making better than human brains.
Re: AI outperforms law professors in Stanford Law study
#96I'm surprised Stanford Law would go along with this over-reaching press release title. How about "For common first-year contracts-law questions, law professors preferred AI-generated answers to professor-generated answers"
Re: AI outperforms law professors in Stanford Law study
#97What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated…
LLMs hallucinate, because humans hallucinate.
Asking the LLM in a way where it annotates its sources, it can greatly increase the pattern matching to closely simulate logic, just like in humans.
I understand the question of why did you say this, not that, I have seen other ways of asking that which do not seem to trigger the LLMs over-response in the other direction.
Re: AI outperforms law professors in Stanford Law study
#98As a software engineer I have some intuition for what the risks are of letting agents do some tasks vs others. I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
IME so far (as both a lawyer and a software engineer), LLM error rates when drafting code and legal documents are reasonably comparable, but it's more problematic in the legal context because legal documents do not benefit from many of the structural safeguards available for code. For legal documents, there are no automated tests, no static typing, no test environments, no logging/observability instrumentation, no sandboxing.
The time lag between drafting and "deployment" also makes for much less effective, much more expensive debugging loops. You can deploy your code to prod in seconds, see an error pop up in the logs, and immediately start debugging. But it will take at a minimum days and frequently as long as several years before an error in a contract or a court filing will be detected, and often the error is beyond correction at that point. Thus, the errors are both more difficult to detect and to resolve.
And the consequences of error are often much greater, both because they are not correctable and because a legal error may risk someone's life, liberty, or substantial property. Although that's not categorically the case, obviously bugs in certain safety critical systems can be as bad or even worse than legal mistakes. But in general, most software is lower stakes than most legal writing.
On the flip side, LLMs do seem to do a better job with basic style and structure for legal documents compared to code. Things like following IRAC format, citing assertions of law (although hallucination remains an issue), and writing comprehensible sentences. These would be the equivalents in code to best practices like good comments, cohesion, consistent use of design patterns, test coverage, clear variable names, DRY, etc. Although the better performance on those more qualitative metrics may just be because even the longest legal documents are typically simpler in structure and have fewer lines of text than a large, complex codebase. Or maybe it's because LLMs are trained on natural language text more than on code. Or because natural language is more forgiving than code, in that minor variation in diction or grammar is unlikely to have any significant effect on how the document is interpreted, whereas even single character errors in code can have enormous effects.
Re: AI outperforms law professors in Stanford Law study
#99> In a blind evaluation of nearly 3,000 anonymized comparisons, professors rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups. 75% win rate seems pretty good! Paper link: https://law.stanford.edu/wp-content/uploads/2026/06/salinas_...
I wonder to what degree the AI was just better at communicating. My experience with attorneys is that they are often some of the worst writers.
The writing is always fluid and grammatically flawless. This carries much more weight with us than we believe. I know the illusion well from decades of grading college papers. Many of the highest quality students use English as a second language, and I know this, but an American well trained in writing, grammar, spelling always gives an impression of superiority. (Being well trained in writing, grammar, spelling etc is of course high merit, which is how the illusion forms - it is basically an illusion of global 'intelligence')
Re: AI outperforms law professors in Stanford Law study
#100As a software engineer I have some intuition for what the risks are of letting agents do some tasks vs others. I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
IME so far (as both a lawyer and a software engineer), LLM error rates when drafting code and legal documents are reasonably comparable, but it's more problematic in the legal context because legal documents do not benefit from many of the structural safeguards available for code. For legal documents, there are no automated tests, no static typing, no test environments, no logging/observability instrumentation, no sa…
Well this is largely the fault of law itself. especially english style law. A legal, parseable code, in which not every single tiny municipality (some less than 1 square mile) has their own set of rules and laws, not all published or available - but which citizens are expected to abide by of course - how could we expect AI to do well and not some typical TV southern lawyer who knows the judge?