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I am currently writing my master thesis on predictive policing using machine learning. Working with local police in Norway. Got a bunch of papers and articles you might find interesting. Hit me up: michaedm@stud.ntnu.no
Earlier quoted context omitted.
All I want to predict is time periods/locations which are vulnerable. Nothing more than that.
Racism is morally wrong but not mathematically wrong. P(criminal|black) > P(criminal), but if you observe that someone has black skin and treat them poorly because of it, you've done a bad thing. It doesn't matter that you were just following Bayesian reasoning because you're still hurting someone on the basis of something they can't control. Lady Justice doesn't wear a blindfold as a fashion accessory. Discarding in…
I never noticed that before. Thanks for pointing this out!
In any case, you shouldn't neglect the subtle but important sources of bias those pre-crime models can have. Here's an interesting talk about it: https://www.youtube.com/watch?v=MfThopD7L1Y Basically, one instance of bias is the fact that many crime-prediction models are trained on police data, which means they will predict crime in places more often targeted by the police anyway. Then the model predictions even ampl…
In any case, you shouldn't neglect the subtle but important sources of bias those pre-crime models can have. Here's an interesting talk about it: https://www.youtube.com/watch?v=MfThopD7L1Y Basically, one instance of bias is the fact that many crime-prediction models are trained on police data, which means they will predict crime in places more often targeted by the police anyway. Then the model predictions even ampl…
So if the AI identifies insider trading at trading firms, banks, etc., we should beware that this would create a feedback loop to look more into the investment and banking sectors and will ignore the mom and pop insider traders? That they go where crime is rampant over where it isn't and that could be a bad thing?
If you train an AI on data that solely consists of trading firms & banks, you should recognize that it's an AI biased towards detecting activity at trading firms & banks, and that it might be lacking in other areas.
It becomes dangerous when such an AI is marketed and assumed as unbiased and the source of truth for detecting all insider trading activity.
Careful! Your crime predictor might unfairly conclude that men are more likely to commit crimes than women.
In any case, you shouldn't neglect the subtle but important sources of bias those pre-crime models can have. Here's an interesting talk about it: https://www.youtube.com/watch?v=MfThopD7L1Y Basically, one instance of bias is the fact that many crime-prediction models are trained on police data, which means they will predict crime in places more often targeted by the police anyway. Then the model predictions even ampl…
You can listen to an interview she does on econtalk -- interesting to learn more about the hidden biases.
http://www.econtalk.org/archives/2016/10/cathy_oneil_on_1.ht...
Careful! Your crime predictor might unfairly conclude that men are more likely to commit crimes than women.
Statistics have been consistent in reporting that men commit more criminal acts than women.[1][2] Self-reported delinquent acts are also higher for men than women across many different actions.[3] Burton, et al. (1998) found that low levels of self control are associated with criminal activity.[4] Many professionals have offered explanations for this sex difference. Some differing explanations include men's evolutionary tendency toward risk and violent behavior, sex differences in activity, social support, and gender inequality.
The code is open-sourced in an R Notebook: http://minimaxir.com/notebooks/predicting-arrests/
The model performance isn't great enough to usher in precrime, even in the best case. There are likely better approaches nowadays. (e.g. since the location data is spatial, a convolutional neural network might work better.)
Palantir already does all this on a massive scale for the US govt. Want to affect future crime in a positive way? Solve the problems that contribute to it.
Not that you asked.
Having worked in law enforcement at various levels (state and federal) in a prior professional life, I can attest to the differences in what gets reported and how based upon who was working or supervising and where they were assigned. Humans are simply not reliable reporters for this kind of data. No matter how hard we try to make the reports plain and standardized our biases, one way or another, will always seep in.