Machine learning in the judicial system is a bad idea
palladiummag.com
palladiummag.com
But that doesn’t mean the talk about using ML to cut human empathy out of more and more of the justice system is just “hype”. There’s no substance to the techniques proposed, that’s true. They are more dangerously biased than the humans they are meant to replace. But that doesn’t mean that these systems aren’t going to be purchased and implemented if we don’t push back harder against the general AI hype that pervades VC tech culture in general.
ML can recommend a movie to watch or a restaurant nearby, and when it fails terribly and reinforces biases and misunderstandings like it always does, the cost to society is minimal. But when you put people’s lives in its hands, the consequences of the inevitable failures are far more damaging.
I'd go further and say we should pay a lot of attention to this too. The subtle ways in which recommendation engines change what cultural goods we consume, including journalism, might very well have some pretty significant effects.
But yes, to essentially beta test rudimentary statistical models in actual court rooms or police stations were people's legal rights are at stake is pretty much insane.
On paper, a ML approach to calculating risk of recidivism can make sense if you consider it as a silicon-based approximation of the criminal brain in making totally rational self-serving responses.
One caveat: even if you have a perfect ML system... never forget the 'human factor' when using these tools. A single fat-finger by a clerk in San Francisco lead to the release of a defendant who went on to commit murder in 2017. https://abc7news.com/details-of-mistaken-release-of-sf-twin-...
It works with recognition. It helps us sort through millions of page files looking for missing documents, both faster and with higher hit rate than using people. It helps us determine how much water is in drone photographed land areas based on the colours of the vegetation. Stuff like that.
Where it absolutely doesn’t work is for prediction and analysis.
Well, it’s not that it doesn’t work in BI as such. I kinda does, but we’ve been working with analysis and BI for 25-50 years. IBM wanted to sell us Watson analytics for BI as an example. We let them run their magic on some of our non-sensitive datasets and they came up with a range of interesting BI models and metrics. Every one of them were far, far inferior to our current human build BI setup, however, and a subscription to them would cost us around three full time analysts.
Prediction is much worse. The probability just isn’t there to use it for anything that isn’t as harmless as advertising. Worse than that, almost every model turns out biased.
So I guess you can say that I agree with you. It’s mostly just hype, but those parts that aren’t hype are incredibly useful.
IBM is largely to blame for that misunderstanding since they brand all of their offerings "Watson" and describe it as if it's one system; so if their system doesn't work, maybe none will. In reality, it's dozens of pre-implemented algorithms with varying degrees of quality. Importantly, how the data interacts with those algorithms requires domain expertise for the data and algorithms so it can be hit-or-mess if that expertise is not properly applied.
Civil cases could benefit enormously from ML, helping judges with rulings suggestions based on jurisprudence.
What makes you think “ML” would even be able to do that?
If, instead, you are thinking of learning from case law, let's consider patents. There is a lot of case law to be used in training from the Eastern District of Texas...
> The legitimacy of the judicial system is based on its status as a well-tested, publicly examinable, and heavily scrutinized tradition of practice. A shift to an unaccountable, opaque software system throws that basis away.
To me this is the fundamental problem with ML poisoning judicial systems: For better or for worse, we are abdicating our responsibility. Justice ain't easy. Automation should free up our time for more important (less automatable) tasks like justice and politics.
1. Tell you the chance you or members of your family will have a criminal case. Early warning system - do I want to leave this area?
2. Tell you what your chances are of conviction/time you will have to serve if convicted once you have a case.
Once it gets into tax law it might be really interesting.
A random forest can easily outperform a hand-made decision tree—and this is the important part—if it is set up correctly and you use a sufficient quantity of high quality data. Linear models have also been used for decades because they work.