The real question for me is: are they less reliable than human judges? Probably yes. But I favor a relative measurement to humans than a plain statement like that.
I think the main difference is an AI judge may provide three different rulings if you just ask it the same thing three times. A human judge is much less likely to be so "flip-floppy".
You can observe this using any of the present-day LLM's - ask it an architectural/design question, provide it with your thoughts, reasoning, constraints, etc... and see what it tells you. Then... click the "Retry" button and see how similar (or dissimilar) the answer is. Sometimes you'll get a complete 180 from the prior response.
Humans flip-flop all the time. This is a major reason why the Meyers-Briggs Type Indicator does such a poor job of assigning the same person the same Meyers-Briggs type on successive tests.
It can be difficult to observe this fact in practice because, unlike for an LLM, you can't just ask a human the exact same question three times in five seconds and get three different answers, because unlike an LLM we have memory. But, as someone who works with human-labeled data, it's something I have to contend with on a daily basis. For the things I'm working on, if you give the same annotator the same thing to label two different times spaced far enough apart for them to forget that they have seen this thing before, the chance of them making the same call both times is only about 75%. If I do that with a prompted LLM anotator, I'm used to seeing more like 85%, and for some models it can be possible to get even better consistency than that with the right conditions and enough time spent fussing with the prompt.
I still prefer the human labels when I can afford them because LLM labeling has plenty of other problems. But being more flip-floppy than humans is not one that I have been able to empirically observe.
We're not talking about labeling data though - we're talking about understanding case law, statutory law, facts, balancing conflicting opinions, arguments, a judge's preconceived notions, experiences, beliefs etc. - many of which are assembled over an entire career.
Those things, I'd argue, are far less likely to change if you ask the same judge over and over. I think you can observe this in reality by considering people's political opinions - which can drift over time but typically remain similar for long durations (or a lifetime).
In real life, we usually don't ask the same judge to remake a ruling over and over - our closest analog is probably a judge's ruling/opinion history, which doesn't change nearly as much as an LLM's "opinion" on something. This is how we label SCOTUS Justices, for example, as "Originalist", etc.
Also, unlike a human, you can radically change an LLM's output by just ever-so-slightly altering the input. While humans aren't above changing their mind based on new facts, they are unlikely to take an opposite position just because you reworded your same argument.
I think that that gets back to the whole memory thing. A person is unlikely to forget those kinds of decisions.
But there has been research indicating, for example, that judges' rulings vary with the time of day. In a way that implies that, if it were possible to construct such an experiment, you might find that the same judge given the same case would rule in very different ways depending on whether you present it in the morning or in the afternoon. For example judges tend to hand out significantly harsher penalties toward the end of the work day.
I’d think there’s also a key adversarial problem: a human judge has a conceptual understanding and you aren’t going to be able to slightly tweak your wording to get wildly different outcomes the way LLMs are vulnerable to.
> The real question for me is: are they less reliable than human judges?
I'd caution that it's never just about ratios: We must also ask whether the "shape" of their performance is knowable and desirable. A chess robot's win-rate may be wonderful, but we are unthinkingly confident a human wouldn't "lose" by disqualification for ripping off an opponent's finger.
Would we accept a "judge" that is fairer on average... but gives ~5% lighter sentences to people with a certain color shirt, or sometimes issues the death-penalty for shoplifting? Especially when we cannot diagnose the problem or be sure we fixed it? (Maybe, but hopefully not without a lot of debate over the risks!)
In contrast, there's a huge body of... of stuff regarding human errors, resources we deploy so pervasively it can escape our awareness: Your brain is a simulation and diagnostic tool for other brains, battle-tested (sometimes literally) over millions of years; we intuit many kinds of problems or confounding factors to look for, often because we've made them ourselves; and thousands of years of cultural practice for detection, guardrails, and error-compensating actions. Only a small minority of that toolkit can be reused for "AI."
I do think you've hit the heart of the question, but I don't think we can answer the second question.
We can measure how unreliable they are, or how susceptible they are to specific changes, just because we can reset them to the same state and run the experiment again. At least for now [1] we do not have that capability with humans, so there's no way to run a matching experiment on humans.
The best we can do it is probably to run the limited experiments we can do on humans -- comparing different judge's cross-referenced reliability to get an overall measure and some weak indicator of the reliability of a specific judge based on intra-judge agreement. But when running this on LLMs they would have to keep the previous cases in their context window to get a fair comparison.
> The real question for me is: are they less reliable than human judges?
I've spent some time poking at this. I can't go into details, but the short answer is, "Sometimes yes, sometimes no, and it depends A LOT on how you define 'reliable'."
My sense is that, the more boring, mechanical and closed-ended the task is, the more likely an LLM is to be more reliable than a human. Because an LLM is an unthinking machine. It doesn't get tired, or hangry, or stressed out about its kid's problems at school. But it's also a doofus with absolutely no common sense whatsoever.
Judges can reason according to principles, and explain this reasoning. LLMs cannot (but they can pretend to, and this pretend chain-of-thought can be marketed as "reasoning"!; see https://news.ycombinator.com/item?id=44069991)
There are technical quirks that make LLM judges particularly high variance, sensitive to artifacts in the prompt, and positively/negatively-skewed, as opposed to the subjectivity of human judges. These largely arise from their training distribution and post-training, and can be contained with careful calibration.
AIs are inferior to humans at their best, but superior to humans as they actually behave in society, due to decision fatigue and other constraints. When it comes to moral judgment in high stakes scenarios, AIs still fail (or can be made to fail) in ways that are not socially acceptable.
Compare an AI to a real-world, overworked corporate decision maker, though, and you'll find that the AI is kinder and less biased. It still sucks, because GI/GO, but it's slightly better, simply because it doesn't suffer emotional fatigue, doesn't take as many shortcuts, and isn't clouded by personal opinions since it's not a person.
The real question for me is: are they less reliable than human judges? Probably yes. But I favor a relative measurement to humans than a plain statement like that.