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Agreed, where do a I sign up. With the proliferation of license plate readers and other surveillance cameras in the last 5 years it's a matter of time before th

Agreed, where do a I sign up. With the proliferation of license plate readers and other surveillance cameras in the last 5 years it's a matter of time before this creeps out to full algorithmic 'risk scores' for when you have encounters with enforcement. For some that may mean just getting shot [0], while they think someone risky might be in the vehicle, based on association. Or other harassment. [0] https://www.latimes.com/local/lanow/la-me-ln-no-charges-lapd-shooting-newspaper-delivery-women-dorner-manhunt-20160127-story.html https://www.latimes.com/local/lanow/la-me-ln-no-charges-lapd...

tastygreenapple 7y ago
It sounds preferable to calculate risk scores deterministically rather than rely on Law Enforcement's discretion. Risk scores are computed for every traffic stop, wouldn't you want to take that process away from the office and give it to a computer?
- freeone3000 7y ago
  No! Because, for some reason that has no basis in reality, people think computers are less biased than humans! They trust computers OVER PEOPLE, even if the computer is racist, or just wrong. Humans are accountable, changeable, cross-examinable. Computer systems are not. Please do not give enforcement systems over to computer systems, and have the bias calcified forever.
  - freeopinion 7y ago
    I'm not arguing for or against computers vs humans, but your argument is puzzling to me. Computer systems are accountable, changeable, and cross-examinable. Arguably more so than humans. I do stipulate that computer systems can be racist or otherwise biased and can be "just wrong".
    - ddxxdd 7y ago
      I fully agree; a neural network can be continuously trained. Software can continuously perform A/B testing and change its mind when its current procedure proves inefficient. The human mind tends to be set in its ways.
      - freeone3000 7y ago
        But it doesn't. Current systems do not do this, but people trust in them because machine learning is "more accurate", somehow. It's a huge problem managing expectations around a black box. Online training does not happen in any form at scale. These "fair" systems are, by and large, nonsense.
- lotu 7y ago
  It really sounds preferable to me, even if the initial versions of these algorithms are biased. They will provide a concrete thing against which to test for bias and improve against. It is easily verifiable if your changes to an algorithm have removed the bias you are worried about. Conversely it is basically impossible to tell if your training on a human has changed their biases in the field, sort of just having them do their job again and see if they are biased.
  - vkou 7y ago
    > They will provide a concrete thing against which to test for bias and improve against. The history of junk forensic science has pretty clearly demonstrated that nobody in law enforcement cares about testing their biases, or improving the processes. If it gets them results, they'll use it.
    - acct1771 7y ago
      When you're a hammer, your metric is how many nails you...nail.
    - int_19h 7y ago
      Law enforcement is general is not good at self-accountability. That accountability has to be rigorously enforced from the outside. With algorithms, I think it can be done... but given how subtle the negative effects can be, the process must be stringent and err on the side of caution. In other words, any algorithm should be assumed to be biased until proven otherwise, and with a relatively high bar at that. There should be clear technical standards for what constitutes a valid proof. The testing process should be reproducible by third parties. And there should be a straightforward way to challenge either the process itself, or its results in any particular case. In practice, given how low our bar for law enforcement accountability is today in other cases - including those where people die or are badly hurt - I'd say that we need to fix those issues first before talking about face recognition and its regulation in this context. And until then, it should be off-limits, because the potential for abuse is so high.
      - 7y ago
- vkou 7y ago
  > Risk scores are computed for every traffic stop, wouldn't you want to take that process away from the office and give it to a computer? What I want is this process to be cross-examinable. You can't cross-examine an algorithm in any meaningful way at a jury trial. You can't cross-examine a neural network at all. You know all the bullshit about how police dogs are used to conduct illegal searches, and how you can't cross-examine a dog? It'll be like that, but much, much worse.
  - aey 7y ago
    No, you can challenge the validity of the risk assessment. Bayes is used in jury trials all the time. https://en.m.wikipedia.org/wiki/Bayesian_inference#Applications https://en.m.wikipedia.org/wiki/Bayesian_inference#Applicati... What we have today is institutional racism based on people’s gut reactions. An NN can be tested and shown that given all other factors it risks race, or any other protected class higher than it should.
    - JohnFen 7y ago
      > What we have today is institutional racism based on people’s gut reactions. Institutional racism is not based on people's gut reactions. Institutional racism is based on flawed algorithms. Not computer algorithms in this case, but bureaucratic ones. I don't see how a flawed computer algorithm would be any easier to correct than a flawed bureaucratic algorithm.
      - aey 7y ago
        Because we can black box test the “risk assessment” that whoever sells to police departments. We can’t black box test bureaucracies.
        JohnFen 7y ago
        Testing the results of the algorithms doesn't address the problem of institutional racism, though. Any bureaucracy will simply use the results that the algorithm supplies in a way that maintains the behavior that the bureaucracy is already programmed to maintain. To address things like institutional racism, you have to change the institution. That's a human problem for which there is no technological solution (although technology can certainly be one of the tools used to create a larger change).
- eecc 7y ago
  You are aware that AI programs reflect the prejudices of the organizations that develop them? https://science.sciencemag.org/content/356/6334/183.full https://science.sciencemag.org/content/356/6334/183.full
  - gliboc 7y ago
    Yes, and it is well known that Tesla's self-driving program reproduces Elon Musk's reckless driving habits... /s I'm just intervening to point out that what you're saying is not what the paper you linked implies. Its title is "Semantics derived automatically from language corpora contain human-like biases", and the general conclusion you could derive from it is that AI programs reflect the stereotypes ... of the data they are trained with. This is why they use the right word: stereotype, instead of the charged word you used: prejudice.
    - eecc 7y ago
      And who selects the training set?
      - gliboc 7y ago
        Do you not mind that your point isn't reflected in the paper you linked?
  - tastygreenapple 7y ago
    I"m not aware of that result, can you help connect your paper to that claim?
Spooky23 7y ago
This already happens. See: https://www.vigilantsolutions.com/products/license-plate-recognition-lpr/ https://www.vigilantsolutions.com/products/license-plate-rec... Basically, you punch in a plate and get alerts, nationwide. It's likely that facial recognition technology can associate some car passengers to a vehicle as well. See: https://www.aclu.org/blog/free-future/license-plate-scanners-also-taking-photos-drivers-and-passengers https://www.aclu.org/blog/free-future/license-plate-scanners... I don't know this, but I would guess that you'd attract attention if you started driving from South Florida and rolled through the wrong neighborhood in a place like Philly or DC on your way home to Boston.

tastygreenapple 7y ago

It sounds preferable to calculate risk scores deterministically rather than rely on Law Enforcement's discretion. Risk scores are computed for every traffic stop, wouldn't you want to take that process away from the office and give it to a computer?

freeone3000 7y ago
No! Because, for some reason that has no basis in reality, people think computers are less biased than humans! They trust computers OVER PEOPLE, even if the computer is racist, or just wrong. Humans are accountable, changeable, cross-examinable. Computer systems are not. Please do not give enforcement systems over to computer systems, and have the bias calcified forever.
- freeopinion 7y ago
  I'm not arguing for or against computers vs humans, but your argument is puzzling to me. Computer systems are accountable, changeable, and cross-examinable. Arguably more so than humans. I do stipulate that computer systems can be racist or otherwise biased and can be "just wrong".
  - ddxxdd 7y ago
    I fully agree; a neural network can be continuously trained. Software can continuously perform A/B testing and change its mind when its current procedure proves inefficient. The human mind tends to be set in its ways.
    - freeone3000 7y ago
      But it doesn't. Current systems do not do this, but people trust in them because machine learning is "more accurate", somehow. It's a huge problem managing expectations around a black box. Online training does not happen in any form at scale. These "fair" systems are, by and large, nonsense.
lotu 7y ago
It really sounds preferable to me, even if the initial versions of these algorithms are biased. They will provide a concrete thing against which to test for bias and improve against. It is easily verifiable if your changes to an algorithm have removed the bias you are worried about. Conversely it is basically impossible to tell if your training on a human has changed their biases in the field, sort of just having them do their job again and see if they are biased.
- vkou 7y ago
  > They will provide a concrete thing against which to test for bias and improve against. The history of junk forensic science has pretty clearly demonstrated that nobody in law enforcement cares about testing their biases, or improving the processes. If it gets them results, they'll use it.
  - acct1771 7y ago
    When you're a hammer, your metric is how many nails you...nail.
  - int_19h 7y ago
    Law enforcement is general is not good at self-accountability. That accountability has to be rigorously enforced from the outside. With algorithms, I think it can be done... but given how subtle the negative effects can be, the process must be stringent and err on the side of caution. In other words, any algorithm should be assumed to be biased until proven otherwise, and with a relatively high bar at that. There should be clear technical standards for what constitutes a valid proof. The testing process should be reproducible by third parties. And there should be a straightforward way to challenge either the process itself, or its results in any particular case. In practice, given how low our bar for law enforcement accountability is today in other cases - including those where people die or are badly hurt - I'd say that we need to fix those issues first before talking about face recognition and its regulation in this context. And until then, it should be off-limits, because the potential for abuse is so high.
    - 7y ago
vkou 7y ago
> Risk scores are computed for every traffic stop, wouldn't you want to take that process away from the office and give it to a computer? What I want is this process to be cross-examinable. You can't cross-examine an algorithm in any meaningful way at a jury trial. You can't cross-examine a neural network at all. You know all the bullshit about how police dogs are used to conduct illegal searches, and how you can't cross-examine a dog? It'll be like that, but much, much worse.
- aey 7y ago
  No, you can challenge the validity of the risk assessment. Bayes is used in jury trials all the time. https://en.m.wikipedia.org/wiki/Bayesian_inference#Applications https://en.m.wikipedia.org/wiki/Bayesian_inference#Applicati... What we have today is institutional racism based on people’s gut reactions. An NN can be tested and shown that given all other factors it risks race, or any other protected class higher than it should.
  - JohnFen 7y ago
    > What we have today is institutional racism based on people’s gut reactions. Institutional racism is not based on people's gut reactions. Institutional racism is based on flawed algorithms. Not computer algorithms in this case, but bureaucratic ones. I don't see how a flawed computer algorithm would be any easier to correct than a flawed bureaucratic algorithm.
    - aey 7y ago
      Because we can black box test the “risk assessment” that whoever sells to police departments. We can’t black box test bureaucracies.
      - JohnFen 7y ago
        Testing the results of the algorithms doesn't address the problem of institutional racism, though. Any bureaucracy will simply use the results that the algorithm supplies in a way that maintains the behavior that the bureaucracy is already programmed to maintain. To address things like institutional racism, you have to change the institution. That's a human problem for which there is no technological solution (although technology can certainly be one of the tools used to create a larger change).
eecc 7y ago
You are aware that AI programs reflect the prejudices of the organizations that develop them? https://science.sciencemag.org/content/356/6334/183.full https://science.sciencemag.org/content/356/6334/183.full
- gliboc 7y ago
  Yes, and it is well known that Tesla's self-driving program reproduces Elon Musk's reckless driving habits... /s I'm just intervening to point out that what you're saying is not what the paper you linked implies. Its title is "Semantics derived automatically from language corpora contain human-like biases", and the general conclusion you could derive from it is that AI programs reflect the stereotypes ... of the data they are trained with. This is why they use the right word: stereotype, instead of the charged word you used: prejudice.
  - eecc 7y ago
    And who selects the training set?
    - gliboc 7y ago
      Do you not mind that your point isn't reflected in the paper you linked?
- tastygreenapple 7y ago
  I"m not aware of that result, can you help connect your paper to that claim?

Spooky23 7y ago

This already happens. See: https://www.vigilantsolutions.com/products/license-plate-recognition-lpr/ https://www.vigilantsolutions.com/products/license-plate-rec... Basically, you punch in a plate and get alerts, nationwide. It's likely that facial recognition technology can associate some car passengers to a vehicle as well. See: https://www.aclu.org/blog/free-future/license-plate-scanners-also-taking-photos-drivers-and-passengers https://www.aclu.org/blog/free-future/license-plate-scanners... I don't know this, but I would guess that you'd attract attention if you started driving from South Florida and rolled through the wrong neighborhood in a place like Philly or DC on your way home to Boston.