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I'm glad you feel this way. So do you agree that it is therefore unacceptable to use such a system even if one accepts the surveillance state that comes with th

I'm glad you feel this way. So do you agree that it is therefore unacceptable to use such a system even if one accepts the surveillance state that comes with them since it demonstrably disadvantages minorities and making them "fair" would only make things worse? Or is this concern more selective?

zo1 7y ago
I'm pretty libertarian, so I lean to simple solutions and my view on "fairness" is very non-complicated. I.e. Yes there may be bias in this technology we use, however, I would say that the important part we should really be focusing on is "how" that tech gets used, not the fact that it isn't perfect in some "unfair" kind of way. All sorts of things around us are unfair to all sorts of sub-groups of people that I don't think we should be going down this path and mixing government intervention in order to resolve it. Yes, it's kinda unfair on some level that the algorithms used don't work so well on women/blacks/children/elderly as well as "white middle-aged men", but it's not unfair in the "government should do something about it" kind of way just yet. If someone gets misidentified, that doesn't mean police should just go out and "arrest" them. Rather we add a person into the mix and have them talk to that person, make a final call. That is actionable counter-case data that we can use to resolve the bias in the training sets. A lot of facial-recognition systems include human components in order to resolve errors. E.g. I've worked with facial-recognition software here in South Africa that was developed by Asian companies where the majority of their data-sets and tests were using Asian individuals, so one has to manage error risk using humans in the loop.
- beardedwizard 7y ago
  How are you managing human bias in this model?
  - zo1 7y ago
    Depends on what kind of bias you want/need to manage? Corruption/bribery is a big risk factor in SA so that is usually the one most people worry about, rather than racial bias. One of the things you have to look at is "how" that bias may manifest and if bias can even be applied in any negative sense in the realm of control you give to the user. If you give someone a task where they are to compare two faces, and they don't like the demographic of that individual, I suppose they might pick incorrectly (because you only gave them true/false type options). But you can build in review processes, reporting, performance feedback, etc to mitigate that. You need not treat it as bias at all, merely bad performance/mistakes that need correcting.
    - beardedwizard 7y ago
      Consider a hypothetical scenario in modern police work: First pass returns a false positive on suspect match, police act on the false positive in a way that results in jail or death due to racial bias. This rarely manifests as a performance issue with easy remediation. I'm not sure adding more humans to the mix is a solution to solving bias problems because humans are unconsciously biased and may be perversely incentivized.
      - zo1 7y ago
        I don't understand? I just gave solutions above how adding humans in the mix solves accuracy and can most-likely preclude bias.

zo1 7y ago

I'm pretty libertarian, so I lean to simple solutions and my view on "fairness" is very non-complicated. I.e. Yes there may be bias in this technology we use, however, I would say that the important part we should really be focusing on is "how" that tech gets used, not the fact that it isn't perfect in some "unfair" kind of way. All sorts of things around us are unfair to all sorts of sub-groups of people that I don't think we should be going down this path and mixing government intervention in order to resolve it. Yes, it's kinda unfair on some level that the algorithms used don't work so well on women/blacks/children/elderly as well as "white middle-aged men", but it's not unfair in the "government should do something about it" kind of way just yet. If someone gets misidentified, that doesn't mean police should just go out and "arrest" them. Rather we add a person into the mix and have them talk to that person, make a final call. That is actionable counter-case data that we can use to resolve the bias in the training sets. A lot of facial-recognition systems include human components in order to resolve errors. E.g. I've worked with facial-recognition software here in South Africa that was developed by Asian companies where the majority of their data-sets and tests were using Asian individuals, so one has to manage error risk using humans in the loop.

beardedwizard 7y ago
How are you managing human bias in this model?
- zo1 7y ago
  Depends on what kind of bias you want/need to manage? Corruption/bribery is a big risk factor in SA so that is usually the one most people worry about, rather than racial bias. One of the things you have to look at is "how" that bias may manifest and if bias can even be applied in any negative sense in the realm of control you give to the user. If you give someone a task where they are to compare two faces, and they don't like the demographic of that individual, I suppose they might pick incorrectly (because you only gave them true/false type options). But you can build in review processes, reporting, performance feedback, etc to mitigate that. You need not treat it as bias at all, merely bad performance/mistakes that need correcting.
  - beardedwizard 7y ago
    Consider a hypothetical scenario in modern police work: First pass returns a false positive on suspect match, police act on the false positive in a way that results in jail or death due to racial bias. This rarely manifests as a performance issue with easy remediation. I'm not sure adding more humans to the mix is a solution to solving bias problems because humans are unconsciously biased and may be perversely incentivized.
    - zo1 7y ago
      I don't understand? I just gave solutions above how adding humans in the mix solves accuracy and can most-likely preclude bias.