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by gravypod
3331 days ago
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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. |
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Sure. I'm not saying my example is the smartest (although there are people trying to use ML to do this to be fair - http://www.fakenewschallenge.org/) and I was unaware of your experience with ML, so I was working under the assumption you had no experience with it and trying to pick a simple example (even if it's dumb) to explain how bias can sneak into models in general, rather than in the specific case of sentencing criminals.
Let's loop back to what you originally said:
> If you're writing a machine learning application to take a dataset and match future inputs to past results I don't see how these biases can sneak into the program
You then go on to describe a number of factors that you think should go into sentencing models that leave plenty of scope for bias:
* "If they meant to do it" - this is a judgement made by a person and clearly reflects the view of the person making the decision.
* If they feel bad about doing it - again, someone has to judge whether someone is legitimately remorseful or is trying to pretend they are to get themselves a lighter sentence.
* "If they have done it before" - This will reflect things like policing tactics. For instance poorer areas might be subject to higher rates of policing especially in areas adopting the broken windows theory of policing (https://en.wikipedia.org/wiki/Broken_windows_theory#New_York...) and often in these areas petty crimes are cracked down on more frequently. This means that people are more likely to have run ins with the law, meaning they're less likely to get jobs due to convictions showing up in background checks, which in turn increases their likelihood to reoffend.
* What severity this crime is - I'm not sure what you mean by this. Do you mean e.g. murder being more severe than petty theft, or things like how severe an assault was? I'm assuming the latter since the former is often just covered by things like sentencing guidelines anyway. If someone commits an assault, then how do you rate this in a way that a model can understand? How do you ensure consistency across different cases and judges?
At any rate, the point of these models is usually to remove the biases that judges might have about people of certain backgrounds from sentencing guidelines and produce a score that informs the likelihood of the convict reoffending (I believe), so your proposal isn't how this works in practice. In practice they're trying to avoid exactly these kinds of subjective assessments you proposed and replace them with supposedly objective predictors for the likelihood of the person in question to reoffend. From the article linked from the post: https://www.propublica.org/article/machine-bias-risk-assessm...
> Northpointe’s software is among the most widely used assessment tools in the country. The company does not publicly disclose the calculations used to arrive at defendants’ risk scores, so it is not possible for either defendants or the public to see what might be driving the disparity. (On Sunday, Northpointe gave ProPublica the basics of its future-crime formula — which includes factors such as education levels, and whether a defendant has a job. It did not share the specific calculations, which it said are proprietary.)
> Northpointe’s core product is a set of scores derived from 137 questions that are either answered by defendants or pulled from criminal records. Race is not one of the questions. The survey asks defendants such things as: “Was one of your parents ever sent to jail or prison?” “How many of your friends/acquaintances are taking drugs illegally?” and “How often did you get in fights while at school?” The questionnaire also asks people to agree or disagree with statements such as “A hungry person has a right to steal” and “If people make me angry or lose my temper, I can be dangerous.”
Given that independent research seems to confirm that the company's model seems to favour higher sentences for people of colour, it's pretty clear from that description where biases could sneak in to the model, I hope?