[ralusek]

I assume that the LSI-R is something that is actually trained based off of how much those factors actually predict the rate of recidivism, though, no? If friends/family/neighbors who have committed crimes is an accurate predictor of recidivism, the fact that black Americans in the inner city have more friends/family/neighbors who have committed crimes does not make the model racist. They're either good predictors or they're not. A black kid in the inner city with friends/family/neighbors who have committed crimes very likely does have a higher rate of recidivism than a white kid in the suburbs, and if this weren't true, but was being predicted by the model, then this would simply be a bad model. If it turns out that there are many black kids who happen to live near neighbors who've committed crimes, but actually do not have a higher rate of recidivism, then the model is as racist as it is using a poorly correlated indicators of recidivism.

Your indicator for whether or not a model is racist cannot simply be that the model produces outputs that are delineated by race in such a manner that is unpalatable. So long as the model is actually not using race as a means of predicting outcomes, though, any behavior that is racist would simply be due to including poor features.

[chongli]

I think you’re still missing the point. Whether the model is accurate or not is beside the point. A completely accurate model may indeed show a higher recidivism risk for an inner city kid compared to one from the suburbs. If it’s used in sentencing or other life-affecting decisions then it’s going to amplify historical injustices.

People commit more crimes when they have less opportunity. People have less opportunity when they grow up in high crime neighbourhoods. This is a negative feedback loop which was started by slavery and accelerated by segregation and redlining.

It’s not enough to use a hands-off approach. To correct the problem requires an active push in the opposite direction, to restore opportunity and break the cycles.

Edit: Think of it this way. You and some friends are playing Monopoly, drinking a few beers and having a great time. An hour and a half into the game (we all know games of Monopoly can last 4 hours or more), you discover one of your friends has been cheating. Now what?

He says "Sorry everyone! I'll stop cheating now and everything will be fine."

Is that true? Of course not. The proceeds from cheating may have been used to acquire the orange properties and maybe even put houses up on them. Every time you and the other friends land on those properties you end up paying rent to the previously cheating friend. Rent that he should not be collecting because those assets were acquired by cheating.

This is what it's like to have historical injustices continue to perpetuate into the future.

[hello_marmalade]

Yes, but you can't build it into your model. In this example, you would end up with a result of putting people back into these communities who have a high level of recidivism. You are actively not avoiding an actual issue because of perceived racial injustice when the issue is not racial.

This is the problem with processing our world down racial lines. You're trying to correct for a historical injustice. The fact that race factors into the circumstance of why people are where they are right now doesn't change the fact that those variables lead to recidivism. It's not racist. It's accurate.

If you want to fix the problem, then you need to fix the underlying issues, which tend to be economic. Those economic issues stem from an issue that affects all races, and therefore splitting it across racial lines only serves to reduce the possibility of actual change.

All you're doing when you try to account for historical injustice is slapping a band-aid on a deeper issue.

(Edit: Grammar)

[chongli]

I agree with you when it comes to the model: the model should be as accurate as possible. The big question is what to do with the model. The way it's being used now, the model is kind of a self-fulfilling prophecy. A prediction of high recidivism risk leads to a longer sentence which increases the likelihood of recidivism. This creates a feedback loop which increases real recidivism risk and the model changes to reflect that. If your goal is to reduce crime in society, then this may be a flawed approach.

[hello_marmalade]

Yes but that's not about race, that's about how we deal with crime as a society. These things aren't being unfairly applied to minority communities and that's the point. The system would be working the same way for a non-minority community, and it does, where the economic situation is similar.

That's why the racial angle is a waste of everyone's time and energy. It's not the relevant issue. The more relevant issue is how we deal with crime prevention. Currently, we go with a punishment approach rather than a truly rehabilitative one. This also has a lot to do with economics, and lobbying and private prisons and so on. It's much more complicated than 'everybody's racist'.

[chongli]

The purpose of the term structural racism, instead of just racism, is to distinguish the theory of historical racism and its downstream effects from the category people who hold views of racial superiority.

Besides that, there are plenty of people around today who actually are racist and they are major proponents of punishment-based approaches. If you try to switch to rehabilitation and intervention, they will resist you. They hold views that some races are innately of lower intelligence and have higher criminal tendencies. You aren't going to counteract that pressure by saying "the racial angle is a waste of everyone's time."

[hello_marmalade]

Yes, but what relevance does structural racism have to the model?

That's only true only if you believe the majority of people are racist. I don't.

There's also a number of ways that you don't even have to interact with that argument. You can show that the end results. It's not like we don't already have the studies and statistics that show how to resolve these issues. You can easily say to someone who thinks that way 'ok sure, you go on believing that, but even if you do believe that, there are still better ways to resolve this.'

Also most people are actually pretty receptive to new information if you are capable of packaging it well, and acknowledging their biases without judging them for it.

[tomp]

I don't think this is a good analogy.

In your example, it could be argued that a person who isn't cheating can keep collecting rent on their properties (however "unfair" that might seem) - i.e. the (un)fairness of the current situation (and the degree to which we try to "fix" it) depends on the path used to get there.

In the "inner city kid" example, it doesn't matter how people got there - either due to historical injustices / racism (i.e. "cheating" by the rest of society) or simply because their parents were drunks or criminals or poor or whatever - so, again, race doesn't and shouldn't matter, and helping poor inner city black kids in preference to poor inner city white kids is racism, no other way of putting it.

[ZeroGravitas]

You can have an accurate prediction which also reflects systemic bias.

There was a story recently about NYC cops being given race based targets for arrests. If that data was fed into a system and predictions generated they could be both correct and racist.

That's maybe an extreme example, I think the person you're replying to was trying to illustrate the same thing but with greater indirection between the racism and the arrest.

To give a non-race example, I've heard that ugly people get convicted at a higher rate than good looking people. So a 'hot or not' rating could help predict reoffending conviction rates. I'd assume we would want to adjust our models to avoid that, even though it's not an incorrect prediction.

[hello_marmalade]

But that's not an issue with the model, that's an issue with our response to the model.

Fundamentally there's nothing wrong with this. The somewhat harsh truth is that 'systemic bias' is actually just... statistics. There are a lot of minority criminals. It's not that there's something special about these people that makes them criminals - it's the same thing that makes everybody turn to crime: lack of opportunity, low capacity for upward mobility, limited access to education, and so on. We act as if the information is not accurate, and that this is a result of racism, but the cold truth is that if you were to take a white person and a black person and only look at the likelihood of criminal behavior, the black person is going to come out on top of that. It's just the math.

Where the human element comes in is where we decide what to do about that math. Do we blame the race? Do we utilize these models to preemptively police people on the basis of race? The answer obviously, should be no. That said, our model can give us insights into this. We can take this data and go, 'well we know that it's unlikely that race is the major causal factor here, so what else can we look at?'

This is a much deeper issue, and 'structural racism' is a really bad way to look at it, because it forces you to focus on the racial elements, even if they're not relevant. It's asking for a model that is not representative of reality, because it looks ugly, rather than looking at it for what it is - just data - and figuring out what to do with that data.