I know someone who works in law and deals particularly with an area of US benefits and healthcare law. One of their workflows for lower-level employees at their firm involves taking in documents from healthcare plans and organizations, analyzing them for certain kinds of data, and then importing that data into an internal system they use to analyze and provide guidance on plans. The internal system can contain hundreds of documents for an individual client. All of the documents have the same information (roughly) but in totally diverse formats and styles. Once it's in the system, it's easy to compare and analyze across documents and the research process is much faster.
They recently bought a Claude subscription and began using Claude to do the initial read of the documents and output JSON they can import into their internal systems. The work still must be reviewed by an attorney - Claude is nowhere near making the kinds of judgments a lawyer would make about this content - but it has increased their throughput from 2-3 documents an hour to 8-10 documents an hour by killing the busy work.
LLMs have great advantages for this kind of work - but not for decision-making. I just don't see OpenAI ever admitting that.
(I've left some details intentionally vague because this is a very specific area of law and I don't want my friends to be identified without their consent.)
I see problems in LLMs doing research, making drafts, etc.
I see 3 kinds of potential users.
1. Non legal background people trying to avoid going to a lawyer. They should definitely NOT use an LLMs.
2. Fresh out of law school graduates.
They think they can do fast work, draft quick replies, do fast case research, enh. They should go the traditional route of interning, learning the "art", they probably won't need it because they will learn "how the law works"
3. Established attorneys.
They usually have templates made for most things, over their career, they have established routines of making things fast, maybe it can help them but to say replacing good chunk of work or offloading to LLMs isn't probably going to go well.
There is a reason why interns are made to do grunt work. The "chain of command" is built so they learn on basic stuff, learn to make decisions, etc. Without these basic decisions under their belt, an intern can't take bigger decisions later on. They just won't know....
Might be true today, but I still feel pareto principle applies. These points also sound like gatekeeping a bit and I hope future AI versions prove that law is also essentially something that can be made as programmable as possible for most common answers if the underlying principles and constitution are organised such.
If there is no precedence for X happening when Y , Z, P, Q, R are special combinations , the judge decides and sets precedence. But if another judge interprets it differently with similar set of circumstances it is not considered incorrect I presume especially if in different jurisdiction unless overridden by a higher court. This is my layman understanding but I think this design is wrong and essentially where human subjectivity controls the system.
Subjectivity is not totally avoidable in law but the goal of the legal system should be reduce it as much as possible along with ambiguity.
Your understanding is correct, but where your understanding goes astray is thinking that that's a bug. The subjectivity is a feature. It is not possible to specify what should legally happen for all possible circumstances the entirety of humankind faces. Like, literally not possible. We humans can't even create rigorous specifications for what our programs should do that keep up with their evolving requirements, and you want to make life-ruining decisions set in stone based on a rigorous set of predetermined rules about all possible circumstances the entirety of humanity could face any time in the future in an evolving world?
Law is intentionally not code. The world is not something you can program.
Isn't achieving a rigorous, unique specificarion what civil law tries to achieve? Not that it is a success, but the idea behind it. Sorry if this is a silly question, but this is how I understand it.
No, it doesn't. It aims to provide a fairly comprehensive framework within which judgments can be made, but it is absolutely expected that there are edge cases and ambiguities which are resolved by human judgment of the circumstances.
Since this is a thread about using ChatGPT for law, I should note that this is actually one of the most important reasons you'd rather hire a lawyer than ChatGPT to represent you. A good lawyer or firm will know the local judges and how they tend to lean on ambiguities in their area of expertise, and will be able to use that information to both advise you and tailor their arguments to appeal to the judge's sensibilities. There is an element of unfairness to this, to be sure, but you'd find much more unfairness really quickly with machine-generated judgments because of how messy and non-compressable real world circumstances are.
ivraatiems · · focus · HN ↗
They recently bought a Claude subscription and began using Claude to do the initial read of the documents and output JSON they can import into their internal systems. The work still must be reviewed by an attorney - Claude is nowhere near making the kinds of judgments a lawyer would make about this content - but it has increased their throughput from 2-3 documents an hour to 8-10 documents an hour by killing the busy work.
LLMs have great advantages for this kind of work - but not for decision-making. I just don't see OpenAI ever admitting that.
(I've left some details intentionally vague because this is a very specific area of law and I don't want my friends to be identified without their consent.)
2Gkashmiri · · focus · HN ↗
I see problems in LLMs doing research, making drafts, etc.
I see 3 kinds of potential users.
1. Non legal background people trying to avoid going to a lawyer. They should definitely NOT use an LLMs.
2. Fresh out of law school graduates.
They think they can do fast work, draft quick replies, do fast case research, enh. They should go the traditional route of interning, learning the "art", they probably won't need it because they will learn "how the law works"
3. Established attorneys. They usually have templates made for most things, over their career, they have established routines of making things fast, maybe it can help them but to say replacing good chunk of work or offloading to LLMs isn't probably going to go well.
There is a reason why interns are made to do grunt work. The "chain of command" is built so they learn on basic stuff, learn to make decisions, etc. Without these basic decisions under their belt, an intern can't take bigger decisions later on. They just won't know....
newyankee · · focus · HN ↗
If there is no precedence for X happening when Y , Z, P, Q, R are special combinations , the judge decides and sets precedence. But if another judge interprets it differently with similar set of circumstances it is not considered incorrect I presume especially if in different jurisdiction unless overridden by a higher court. This is my layman understanding but I think this design is wrong and essentially where human subjectivity controls the system.
Subjectivity is not totally avoidable in law but the goal of the legal system should be reduce it as much as possible along with ambiguity.
applfanboysbgon · · focus · HN ↗
Law is intentionally not code. The world is not something you can program.
rayiner · · focus · HN ↗
azan_ · · focus · HN ↗
gf000 · · focus · HN ↗
amunozo · · focus · HN ↗
applfanboysbgon · · focus · HN ↗
Since this is a thread about using ChatGPT for law, I should note that this is actually one of the most important reasons you'd rather hire a lawyer than ChatGPT to represent you. A good lawyer or firm will know the local judges and how they tend to lean on ambiguities in their area of expertise, and will be able to use that information to both advise you and tailor their arguments to appeal to the judge's sensibilities. There is an element of unfairness to this, to be sure, but you'd find much more unfairness really quickly with machine-generated judgments because of how messy and non-compressable real world circumstances are.
amunozo · · focus · HN ↗