I've done some machine learning in the past. I get how you tackle machine learning problems and the first thing that I would say that your proposed topic is not currently possible.
"*Say you're trying to come up with an algorithm that decides whether articles in a data set are "fake news"
(topical, I know).*"
Current AI cannot do this. This would take finding sources, pulling data out of those sources, cross referencing multiple sources, and recurring for those articles to a certain depth.
That's not a good facsimile for deciding sentencing. Sentencing is more like a linear regression classification. You have a history of previous cases where the defendant was found guilty. You then have a pile of factors that played into the judge's decision for sentencing. For example:
* If they meant to do it
* If they feel bad about doing it
* If they did do it (Beyond a reasonable doubt)
* If they have done it before
* What severity this crime is
* ... etc
The judge then uses their experience in law and previous case law as well as statues to find a proper punishment. This is in the form of:
* Time served
* Fines
* Privileges revoked
* Community Service
This would then be fed into a classification engine. You leave all of the existing infrastructure in place (Judge, Jury, Lawers) and just use their decision as input into the sentencing.
Deciding the validity of claims is not within the scope of modern day machine learning (as of 2017). Classification engines are very much in the scope of machine learning of today.
I don't see how case factors could be biased. I don't see how historical cases (when stripped of all identifying information) could be biased. I don't see why a system like this would be bad.
All treatment of everyone would converge into a uniform handling of cases.
That's not a good facsimile for deciding sentencing. Sentencing is more like a linear regression classification. You have a history of previous cases where the defendant was found guilty. You then have a pile of factors that played into the judge's decision for sentencing. For example:
The judge then uses their experience in law and previous case law as well as statues to find a proper punishment. This is in the form of: This would then be fed into a classification engine. You leave all of the existing infrastructure in place (Judge, Jury, Lawers) and just use their decision as input into the sentencing.Deciding the validity of claims is not within the scope of modern day machine learning (as of 2017). Classification engines are very much in the scope of machine learning of today.
I don't see how case factors could be biased. I don't see how historical cases (when stripped of all identifying information) could be biased. I don't see why a system like this would be bad.
All treatment of everyone would converge into a uniform handling of cases.