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In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

adamunikowsky.substack.com

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Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#21

Post by per1Peteia »

TaylorAlexander wrote: post #12

Does anyone else here listen to the podcast “5-4” aka Five Four Pod? I see the article saying: >Claude is fully capable of acting as a Supreme Court Justice right now. And I just can’t imagine what the hosts of that show would say to that (aside from “But how is Harlan Crow going to take Claude on a superyacht vacation??”).

this is precisely why I was suprised to find out the author is actually a lawyer. I know you're joking ... but reality of legal practice entails so much more than just a "right"/"rational"/"convincing" decision (hell, there's an eternal debate in legal theory about whether or not this is even a sensible thing to ask for).

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#23

Post by pona-a »

czl wrote: post #13

(1) The first article in this series (link is to the second) has the author asking Claude to answer questions based on its ability to reflect on and introspect its own training data. Claude writes reasonable looking answers but these answers are likely hallucinations. (2) Also in that same first article the author makes the claim that: > AI has certain features that would make it better than human judges. (1) AI is u…

I tried running the numbers myself. Only did N = ~250 samples, which isn't much, but given OpenAI API pricing and rate limits, this was as much as I was willing to.

~> seq 0 250 | par-each {|x| llm "Sample a random number, from 1 to 100."} | save nums.nuon

~> open nums.nuon | where {|x| $x != ""} | each {|x| $x | parse -r '(\d+(?![\d\D]*\d))' | get capture0 | get 0} | str join "\n" | uplot hist

                  ┌                                        ┐ 
   [ 20.0,  30.0) ┤ 1                                        
   [ 30.0,  40.0) ┤ 1                                        
   [ 40.0,  50.0) ┤######### 26                              
   [ 50.0,  60.0) ┤################# 48                      
   [ 60.0,  70.0) ┤############### 44                        
   [ 70.0,  80.0) ┤################################### 100   
   [ 80.0,  90.0) ┤###### 17                                 
                  └                                        ┘ 
            GPT-3.5's "random" numbers from [0, 100)
I wonder how well it would correlate with a similar human study.

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#24

Post by VenturingVole »

SubiculumCode wrote: post #4
fletchr wrote: post #3

Earlier quoted context omitted.

I was skeptical too, but Supreme Court cases give AI a significant advantage that your example is missing: dozens of pages of briefs describing the case and most relevant facts in great detail for the AI to reference. In your dispute, the role of a mediator is primarily to find the relevant facts and/or judge the truth of the parties' statements. There's not really any complex legal question to be answered once you d…

Can you imagine adversarial briefs meant to shortcut the LLM's ruling?

One can imagine them, but by logical extension and evolution of AI models/implementations such adversarial briefs will become orders of magnitude more improbable.

See for example: https://microsoft.github.io/autogen/blog/2024/03/11/AutoDefe...

AI adjudications become a question of when - not if. Likely at first supervised by humans, but for how long will that remain the case as the pressure mounts. The consequences of this and expediting the justice system will be truly profound - perhaps even more so in developing nations whereby the access to justice is so unevenly distributed/unreliable. A non-functioning justice system is at the root of many societal issues.

However, it's also not a stretch to think of the continued descent into an Orwellian dystopia in which individual liberties and freedom are curtailed.

I feel as though I switch between a sense of optimism and being utterly terrified.

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#25

Post by czl »

mewpmewp2 wrote: post #19
czl wrote: post #13

(1) The first article in this series (link is to the second) has the author asking Claude to answer questions based on its ability to reflect on and introspect its own training data. Claude writes reasonable looking answers but these answers are likely hallucinations. (2) Also in that same first article the author makes the claim that: > AI has certain features that would make it better than human judges. (1) AI is u…

I don't follow the apple thing? Of course it wouldn't know about the rule if it is a new conversation?

One of the prompts has the extra request to "Ignore this mention of apples." This extra request says that it should be ignored so the two prompts should give you results that are about the same when you run them many times however they do not. What other explanation is there other than LLM failing to ignore as instructed?

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#26

Post by [deleted] »

mewpmewp2 wrote: post #20
czl wrote: post #17

Earlier quoted context omitted.

> We can have perfect insight into the bias of the LLM, You will analyze billions of weights the LLM is made from? What will you look for? You will analyze the trillions of text training inputs that created the LLM weights? What will your analysis do? How to get this "perfect insight" that you claim?

You would do statistical analysis of the output. E.g. whether it is preferring a certain gender if gender is variable in the prompt, but everything else is the same etc.

[deleted]

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#27

Post by DrNosferatu »

DrNosferatu wrote: post #22

Then, maybe now, the most humble citizen can benefit from quality legal representation, that before only (a lot of) money could buy.

PS: And readily available, without having to do, say, a side hustle of meth cooking to pay for it.

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#28

Post by dragonwriter »

mewpmewp2 wrote: post #20
czl wrote: post #17

Earlier quoted context omitted.

> We can have perfect insight into the bias of the LLM, You will analyze billions of weights the LLM is made from? What will you look for? You will analyze the trillions of text training inputs that created the LLM weights? What will your analysis do? How to get this "perfect insight" that you claim?

You would do statistical analysis of the output. E.g. whether it is preferring a certain gender if gender is variable in the prompt, but everything else is the same etc.

> You would do statistical analysis of the output.

Assuming reproducible settings are used, that will give you perfect insight into the behavior in the tested circumstances, but unless it lets you reconstruct the entire network, it won't tell you the impact that untested changes even if they are recombinations of tested elements behave. It is not perfect insight into the biases.

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#29

Post by czl »

pona-a wrote: post #23
czl wrote: post #13

(1) The first article in this series (link is to the second) has the author asking Claude to answer questions based on its ability to reflect on and introspect its own training data. Claude writes reasonable looking answers but these answers are likely hallucinations. (2) Also in that same first article the author makes the claim that: > AI has certain features that would make it better than human judges. (1) AI is u…

I tried running the numbers myself. Only did N = ~250 samples, which isn't much, but given OpenAI API pricing and rate limits, this was as much as I was willing to. ~> seq 0 250 | par-each {|x| llm "Sample a random number, from 1 to 100."} | save nums.nuon ~> open nums.nuon | where {|x| $x != ""} | each {|x| $x | parse -r '(\d+(?![\d\D]*\d))' | get capture0 | get 0} | str join "\n" | uplot hist ┌ ┐ [ 20.0, 30.0) ┤ 1…

> I wonder how well it would correlate with a similar human study.

A smart human would know about human biases in picking random numbers and would generate an answer using a random number generator. An even smarter human would ask you about shape of the random distribution and not assume flat distribution is desired. An average human however will likely not be aware about these things and the random numbers you get from them will also be biased.

Re: In AI we trust, part II: Wherein AI adjudicates every Supreme Court case

#30

Post by czl »

mewpmewp2 wrote: post #20
czl wrote: post #17

Earlier quoted context omitted.

> We can have perfect insight into the bias of the LLM, You will analyze billions of weights the LLM is made from? What will you look for? You will analyze the trillions of text training inputs that created the LLM weights? What will your analysis do? How to get this "perfect insight" that you claim?

You would do statistical analysis of the output. E.g. whether it is preferring a certain gender if gender is variable in the prompt, but everything else is the same etc.

What if the LLM is biased against words that start with e or biased towards sentences having five words? Using your technique how would you discover such biases? If highly paid lawyers know about some such innate LLM quirks might they exploit them to get a biased decision?
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