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Of course data contains biases. But again, please read the article I linked; algorithms will have a tendency to correct that bias. The examples in the article

Of course data contains biases. But again, please read the article I linked; algorithms will have a tendency to correct that bias. The examples in the article you link to are not algorithmic bias at all. They consist of: 1) Humans at Facebook manipulating trending results. 2) Google's keyword algorithm (accurately) reflecting the fact that people with black names are more likely to have arrest records. Lets distinguish "bias" from "accurately learning things you wish it wouldn't learn" or "accurately learning things you wish weren't true." None of what I'm saying is remotely controversial. If I told you statistics could detect and correct bias in a mobile phone compass, you'd just think "cool stats bro". Is this article remotely controversial? https://www.chrisstucchio.com/blog/2016/bayesian_calibration_of_mobile_phone_compass.html https://www.chrisstucchio.com/blog/2016/bayesian_calibration... The specific feedback loop you describe - variable detection probability => variable # of detections - can be directly mitigated. For a non-controversial example drawn from sensor networks (sensors report events with a delayed eraction, the longer you wait the more events you detect), see here: https://www.chrisstucchio.com/blog/2016/delayed_reactions.html https://www.chrisstucchio.com/blog/2016/delayed_reactions.ht... (You can find similar examples all over the place. I just link to the ones I wrote because they spring immediately to mind.) In a compass, a sensor network, adtech or other quant finance, the idea that machine learning can fix biased inputs is not remotely controversial. The concept that statistics suddenly stops working to fix racism is just silly anthropomorphism.

wrsh07 10y ago
Aha - I think I see our miscommunication. When you say bias you mean statistical bias. Yes, machine learning is able to correct for that kind of bias - 538's polls forecast is a good example of that. But you don't get to redefine racial bias to be something innocuous. Yes, black names are more likely to have arrest records, but that "fact" is super misleading [1]. Finally, you're talking past me. I'm not saying that statistics is broken. I'm saying that we should be especially mindful of the OPs point when they say this: > So what’s your data being fried in? These algorithms train on large collections that you know nothing about. Sites like Google operate on a scale hundreds of times bigger than anything in the humanities. Any irregularities in that training data end up infused into in the classifier. I think the OP author also has a related post about the kind of bias I'm talking about: http://idlewords.com/talks/sase_panel.htm http://idlewords.com/talks/sase_panel.htm [1]: http://www.huffingtonpost.com/kim-farbota/black-crime-rates-your-st_b_8078586.html http://www.huffingtonpost.com/kim-farbota/black-crime-rates-...
- yummyfajitas 10y ago
  Without getting into a dispute about the definition of "bias", I'm saying that algorithms can accurately measure reality even if input(x=white, all else equal) != input(x=black, all else equal). You are saying that algorithms are accurately measuring a reality you wish were different. I don't disagree with this. The right thing to do is to actually answer unpleasant moral questions like "if blacks are 4x more likely to be dangerous criminals, what should we do about it?" But I guess overloading the word "bias" is a nice substitute for clearly thinking things through.
  - srean 10y ago
    Are you saying that it can form a good estimate of the conditional probability ? I can believe that if the sampling process preserves the conditional. Otherwise one would have to make assumptions about (or in other words, model) the corruption process. The bias compensation machinery then has to be deliberate, wont happen on its own. Some sampling processes do not modify the conditional. In those cases no special machinery would be required.
    - yummyfajitas 10y ago
      tOne approach is to directly model the corruption process. Being the model-based-Bayesian guy I am, this is something I like to do. But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process. In the example in my linked blog post, test scores might be biased against blacks. But race is also redundantly encoded, so the algorithm has enough information to fix the bias completely by accident. Fundamentally what I'm saying here is that bias is a statistics problem and has a statistics solution. Insofar as your complaint is algorithms finding the wrong answer, the solution is better stats. And nothing whatsoever that I've said here would be remotely controversial if the topic were remote sensing.
      - srean 10y ago
        > But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process This is the claim that I am having trouble with. Say I have two random variable X,Y with some joint distribution. If a corruption process can mess with the samples drawn from it, I cannot see how it could possibly recover either the joint or the conditional. Are you saying that the corruption is benign like missing at random or missing completely at random ? Then its much more believable.

wrsh07 10y ago

Aha - I think I see our miscommunication. When you say bias you mean statistical bias. Yes, machine learning is able to correct for that kind of bias - 538's polls forecast is a good example of that. But you don't get to redefine racial bias to be something innocuous. Yes, black names are more likely to have arrest records, but that "fact" is super misleading [1]. Finally, you're talking past me. I'm not saying that statistics is broken. I'm saying that we should be especially mindful of the OPs point when they say this: > So what’s your data being fried in? These algorithms train on large collections that you know nothing about. Sites like Google operate on a scale hundreds of times bigger than anything in the humanities. Any irregularities in that training data end up infused into in the classifier. I think the OP author also has a related post about the kind of bias I'm talking about: http://idlewords.com/talks/sase_panel.htm http://idlewords.com/talks/sase_panel.htm [1]: http://www.huffingtonpost.com/kim-farbota/black-crime-rates-your-st_b_8078586.html http://www.huffingtonpost.com/kim-farbota/black-crime-rates-...

yummyfajitas 10y ago
Without getting into a dispute about the definition of "bias", I'm saying that algorithms can accurately measure reality even if input(x=white, all else equal) != input(x=black, all else equal). You are saying that algorithms are accurately measuring a reality you wish were different. I don't disagree with this. The right thing to do is to actually answer unpleasant moral questions like "if blacks are 4x more likely to be dangerous criminals, what should we do about it?" But I guess overloading the word "bias" is a nice substitute for clearly thinking things through.
- srean 10y ago
  Are you saying that it can form a good estimate of the conditional probability ? I can believe that if the sampling process preserves the conditional. Otherwise one would have to make assumptions about (or in other words, model) the corruption process. The bias compensation machinery then has to be deliberate, wont happen on its own. Some sampling processes do not modify the conditional. In those cases no special machinery would be required.
  - yummyfajitas 10y ago
    tOne approach is to directly model the corruption process. Being the model-based-Bayesian guy I am, this is something I like to do. But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process. In the example in my linked blog post, test scores might be biased against blacks. But race is also redundantly encoded, so the algorithm has enough information to fix the bias completely by accident. Fundamentally what I'm saying here is that bias is a statistics problem and has a statistics solution. Insofar as your complaint is algorithms finding the wrong answer, the solution is better stats. And nothing whatsoever that I've said here would be remotely controversial if the topic were remote sensing.
    - srean 10y ago
      > But if your model is sufficiently expressive you don't need to explicitly build or model the corruption process This is the claim that I am having trouble with. Say I have two random variable X,Y with some joint distribution. If a corruption process can mess with the samples drawn from it, I cannot see how it could possibly recover either the joint or the conditional. Are you saying that the corruption is benign like missing at random or missing completely at random ? Then its much more believable.