Submitted by ADefiniteDescription t3_z1wim3 in philosophy
FaustusC t1_ixdp47m wrote
This is an interesting read. At the same time, it does itself a disservice by looking at the issue through an equity or moral lens.
Let's examine.
Neighborhood A. Neighborhood B.
A has minimal police patrols, minimal police calls, minimal interactions with law enforcement.
B has regular patrols, regular calls and frequent interactions with law enforcement.
It doesn't matter that the area is impoverished, it doesn't matter than the area is primarily minorities. What matters is that's where the crime is so that's where the police go. Why would you allocate resources to an area they wouldn't be used? B gets more calls, so B gets more patrols, so B has more interactions. If A starts seeing an increase, the AI would naturally divert resources accordingly.
This isn't so much an issue of biased data, as much as it's an issue of people not liking what the data shows. And that's something that needs to be admitted. All the AI can do is look at the areas and suggest based on the inputs which area is more likely to have crime.
The site's sources for data also don't regard the actions of the arrested towards the officer at all. If you're not doing anything illegal, you get let go 99% of the time. If you act uncooperative or aggressively you invite attention. Which causes your likelihood of being arrested to skyrocket.
Should we work to solve the root issues? Absolutely. But a LOT of that work needs to come from those areas themselves. You can pump all the funding in the world through them but if the people inside don't want to change, you won't change the statistics. There's some statistics in the article that are closed to banned on reddit. I won't copy them. I think a question we should be asking is: As B, if you know you're more likely to be punished than A for doing something, why would you do it? If I was predisposed to brain bleeds, I wouldn't join boxing. Some of this is personal choice. If I knew I was more likely to get arrested for smoking pot, I wouldn't touch the shit.
Pawn_of_the_Void t1_ixdwxg6 wrote
This assumes the prior data was done without bias firstly. If they are currently overfocusing on one area due to some bias the algorithm will have that baked in due to the data it is given to work with. Secondly, that seems like it would be prone to a feedback loop. More police focus could itself be a reason for more incidents. As was pointed out in the article, similar crimes in a strongly policed area would be more likely to be caught. This would increase numbers in that area and make it look like that area needs more attention, not because there is more crime but because there is more crime already noticed.
elmonoenano t1_ixemnhy wrote
It also makes the mistake of thinking of criminality as some objective thing and not a social construct. You can make loitering a crime, and then make housing extremely dense and without social spaces so that people in an area congregate in public. Which is exactly what the US did with red lining and segregation. So you have people forced to socialize in public spaces and then you criminalize hanging out in those spaces, or drinking there, etc. And now you have a record of different behavior that you can utilize in a "race blind" way, even though historically you know it's very race conscious.
NYPD's Compstat did exactly that and they tried to use it as evidence that the NYPD wasn't enforcing the law in a race biased method.
TheEarlOfCamden t1_ixe22d7 wrote
But if you were training such a model you would obviously want to include in its training data how much time police were spending there already, so it ought to be able to distinguish between an area where there are more arrests because there is more crime from one where there are more arrests because there is more police.
Pawn_of_the_Void t1_ixegtrs wrote
Well, the thing here is you just started talking about it being able to tell why there are more arrests in one area than another. That seems like a hell of a lot more complicated than the prior task of just finding the area where they report the most incidents. Time spent alone isn't a sufficient indicator really, is it? Its a factor and something that can skew the data but you can't just directly decide its the cause from the time spent there data being added in
TheRoadsMustRoll t1_ixdz3q1 wrote
>Neighborhood A. Neighborhood B.
>
>A has minimal police patrols, minimal police calls, minimal interactions with law enforcement.
>
>B has regular patrols, regular calls and frequent interactions with law enforcement.
correction: if you are using algorithms all you can say is "Neighborhood A had minimal police patrols..." because you are always looking into the past.
in the past there were no algorithms. so you start the historical data set where? in the 1940's? 50's? 60's? those were racist days. so were the 80's, 90's and 2000's.
if you don't start with an objective data set then your algorithms will be biased. and with backward-looking algorithms you won't know that a neighborhood profile has changed until its recorded stats are significantly different. in the meantime you'll be letting crimes go unaddressed.
your particularly unsophisticated approach to a very sophisticated technology (which you fail to understand) is at the heart of this issue.
notkevinjohn t1_ixe1tfy wrote
Not really, because you can write the algorithm to have as long (or short) a memory as you want it to have. You could even write an algorithm that gives zero weight to all historical crime data and starts by assigning officers randomly throughout the community, and then it continuously updates that distribution of officers based on the crime data starting only from that randomized initial condition. It's basically just wrong to argue that you have to start with an objective data set, you can start with absolute garbage data and the only effect might be that it takes your algorithm a few extra cycles to get past that and converge on a sensible state.
I don't think the OP failed to understand the technology of algorithms at all, and I've been an embedded systems engineer and programmer for 15 years. I think the OP was absolutely right in pointing out that what we're afraid of is that the systems will end up with coverage maps that look too familiar to us, and we won't want to confront that reality. I don't know if that's the case, but I think it's accurate that it's what people fear is the case.
FaustusC t1_ixe3oaj wrote
100%, spot on.
People are acting like this AI would only speculate off that past history and not constantly update the model.
You could literally feed in historical data that says there's only crime in neighborhood A despite the opposite being true and the AI would correct the issue within a few cycles as you said. The big thing here is these prediction models learn and they only learn off of input. If everything but the location & type of crime was scrubbed from the data, literally no demographic information at all, the results would come out the same.
I think even philosophically we're at a point where we can't even discuss that the data might just be data without people crying foul and it disgusts me. Racism by low expectations is still racism. I grew up in a very, very shitty neighborhood B. I've also lived in Neighborhood As. I can't say A was completely without incident, but comparing the two even off of my anecdotal experiences is night and Day.
I think the biggest incident in A was someone complaining about Horse droppings on the beach and some teens setting a dumpster on fire.
B had someone get shot. Completely anecdotal but still relevant.
notkevinjohn t1_ixe572q wrote
As I've pointed our elsewhere in the thread, I think a lot of people aren't distinguishing between an explicit algorithm, and a machine learning algorithm. I think people in this thread are looking at algorithms as a black box, where you put data in, something incomprehensible happens, and then police go and arrest people. When you have machine learning, it's a non-deterministic process where even the programmer who built the system can't work it backward and say 'this person was arrested because of these inputs to the system.' But there are tons of algorithms that could be developed where the programmer can tell you EXACTLY which inputs lead to a particular result, and the transparency of these algorithms could vastly exceed the transparency of machine learning, and even exceed the transparency of our current human-driven system.
FaustusC t1_ixe5mvt wrote
Tbh, I don't think most of the people even vaguely understand the difference but are thrilled at the opportunity to morally grandstand against a supposed injustice.
FaustusC t1_ixdzzua wrote
Assuming data itself is biased is the heart of this issue and why people shouldn't be allowed to handle it at all.
Claiming "that era was racist" so all data must be discarded is a cop out and ignores the issues.
Data is nothing but points. Acting like Middle class, Median income A and Lower class, low income B will have similar or equal crime rates is insanity and racism. Pretending like A has the same amount of crime, they're just not patrolled is ignorant at best, racist at worst.
rami_lpm t1_ixe8yf0 wrote
> If you're not doing anything illegal, you get let go 99% of the time. If you act uncooperative or aggressively you invite attention.
Sure. No 'walking while brown' type of arrests in this magical neighborhood of yours.
>As B, if you know you're more likely to be punished than A for doing something, why would you do it?
this is straight up victim shaming.
FaustusC t1_ixed3hx wrote
My dude, those are statically miniscule amounts of the arrests. If we counted all of them together over 10 years, they'd be a fraction of a percentage of legitimate stops and arrests.
No, it's common sense. I don't speed Because I don't want to get stopped. I drive a dumb car, in a dumb color with a vanity plate. I already have a target on myself. Why would I give them a legitimate reason to screw with me? If an action is illegal, and you know you're more likely to be punished for commiting it, why would you knowingly take the risk? How is that victim blaming?
rami_lpm t1_ixf3wr6 wrote
I understand it may be so now, but if they use historical data to train the ai, then any racial bias from previous decades, will show.
What if you were targeted not by your actions but by the looks of your car?
All I'm saying is that the training data needs to be vetted by several academic parties, to eliminate as much bias as possible.
FaustusC t1_ixf6rtn wrote
Then I don't think you understand how it works. The Bias will train itself out within a few cycles. Because that's how it works. The AI will start using that "flawed" data and then, as it progresses, will slowly integrate it's new findings into the pool. It may take a few years, but, if policing was misweighted, the AI would allocate the resources where they were needed. If you train an AI to do basic addition, and to know numbers, once it knows enough numbers you can't tell it 1+1=6. If I ask the AI for the number between 7 and 9, it will list off 6+2, 5+3, 4+4 etc. I can tell it 2+3 is the answer, but it will search and say I'm incorrect Because based purely on the data, I cannot be correct. We can compare that to the earlier arguments. The AI can see crime at points X, Y and Z in neighborhood B but crime in Q in neighborhood A.
I am lol. "Yes sir, no sir, here's my license sir, have a nice night."
And I'm saying that letting "academic parties" get their hands on it is going to simply nudge bias the opposite way. Positive bias. That will get us nowhere until the AI fixes itself at which point people will screech that somehow the AI went racist again lol. Academics has a serious issue with bias but that's an entirely different argument.
rvkevin t1_ixgogni wrote
>The AI can see crime at points X, Y and Z in neighborhood B but crime in Q in neighborhood A.
The AI doesn't see that. The algorithm is meant to predict crime, but you aren't feeding actual crime data into the system, you're feeding police interactions (and all the biases that individual officers have) into the system. More data doesn't always fix the issue because the issue is in measuring the data.
FaustusC t1_ixh5ydr wrote
But that's the thing: unless someone's getting hit with completely falsified evidence, the arrest itself doesn't become less valid. It's irrelevant to the data whether or not a crime is uncovered because of a biased interaction or an unbiased one. The prediction model itself will still function correctly. The issue isn't measuring the data, it's getting you to start acknowledging data accuracy. A crime doesn't cease to be a crime just because it wasn't noticed for the right reasons.
rvkevin t1_ixjrv88 wrote
>But that's the thing: unless someone's getting hit with completely falsified evidence, the arrest itself doesn't become less valid.
It still doesn’t represent actual crime; it represents crime that the police enforced (i.e. based on police interactions). For example, if white and black people carry illegal drugs at the same rate, yet police stop and search black people more, arrests will show a disproportionate amount of drugs among black people and therefore devote more resources to black neighborhoods even when the data doesn’t merit that response.
> It's irrelevant to the data whether or not a crime is uncovered because of a biased interaction or an unbiased one.
How is a prediction model supposed to function when it doesn’t have an accurate picture of where crime occurs? If you tell the model that all of the crime happens in area A because you don’t enforce area B that heavily, how is the model supposed to know that it’s missing a crucial variable? For example, speed trap towns that gets like 50% of their funding from enforcing speed limits in a mile stretch of highway. How is the system supposed to know that speeding isn’t disproportionately worse there despite the mountain of traffic tickets given out?
>The issue isn't measuring the data, it's getting you to start acknowledging data accuracy.
How you measure the data is crucial because it’s easy to introduce selection biases into the data. What you are proposing is exactly how they are introduced since you don’t even seem to be aware it’s an issue. It is more than just whether each arrest has merit. The whole issue is that you are selecting a sample of crime to feed into the model and that sample is not gathered in an unbiased way. Instead of measuring crime, you want to measure arrests, which are not the same thing.
notkevinjohn t1_ixebwfn wrote
No it's not, it's game theory. There may be totally valid reasons for doing that thing which might be critical to understand. It's only victim shaming if you start from the assumption that they are doing that thing because they are stupid, or lack self control, or some other undesirable characteristic.
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