4 ms·

Chris Stucchio's point is different from that of the article. The problem is that the word "bias" is overloaded, and used in two different ways. When ProPublica

by scott_s 10y ago

Chris Stucchio's point is different from that of the article. The problem is that the word "bias" is overloaded, and used in two different ways. When ProPublica uses the word bias, they mean that one race is affected in a systematically different way than another race. When Chris Stucchio uses the word bias, he means a statistical method which has a built in bias to achieve a particular result. It is quite possible for both to be true: the statistical methods have no built in bias, but their prediction ends up making different predictions for different races. I find the most reasonable explanation to be that society itself has a racial bias, and society itself is the data generator.

tptacek 10y ago
Yes, this is a broad criticism of ML in public policy: that it's an amplifier for the status quo.
- xapata 10y ago
  > amplifier for the status quo While there are respectable ML folks making those criticisms, the commentary I've read seems more click-bait than science. ML can be used just as traditional statistics to make causal inferences and predict the effect of intervention. There's nothing about ML that reinforces status quo more than traditional statistics, let alone case studies (aka anecdotes) or "common sense."
  - Eridrus 10y ago
    While this is definitely a clickbaity topic, and algorithms are usually better than people there are still real issues that come from seeking accuracy, particularly since real world data is so often biased to begin with. Google had an interesting take on how you could control for some dimensions of bias here https://research.google.com/bigpicture/attacking-discrimination-in-ml/ https://research.google.com/bigpicture/attacking-discriminat...
  - snowwrestler 10y ago
    > There's nothing about ML that reinforces status quo more than traditional statistics Yes but that is not how ML is marketed. It's marketed as way better than traditional statistics, and soon even better than humans. But reality is that a trained system is only as good as the data used to train it.
    - xapata 10y ago
      > only as good But ML can improve upon other methods of interpreting that (biased) data. Thus, in some ways better than traditional statistics and non-mathematical human intuition.

by scott_s 10y ago

tptacek 10y ago

Yes, this is a broad criticism of ML in public policy: that it's an amplifier for the status quo.

xapata 10y ago
> amplifier for the status quo While there are respectable ML folks making those criticisms, the commentary I've read seems more click-bait than science. ML can be used just as traditional statistics to make causal inferences and predict the effect of intervention. There's nothing about ML that reinforces status quo more than traditional statistics, let alone case studies (aka anecdotes) or "common sense."
- Eridrus 10y ago
  While this is definitely a clickbaity topic, and algorithms are usually better than people there are still real issues that come from seeking accuracy, particularly since real world data is so often biased to begin with. Google had an interesting take on how you could control for some dimensions of bias here https://research.google.com/bigpicture/attacking-discrimination-in-ml/ https://research.google.com/bigpicture/attacking-discriminat...
- snowwrestler 10y ago
  > There's nothing about ML that reinforces status quo more than traditional statistics Yes but that is not how ML is marketed. It's marketed as way better than traditional statistics, and soon even better than humans. But reality is that a trained system is only as good as the data used to train it.
  - xapata 10y ago
    > only as good But ML can improve upon other methods of interpreting that (biased) data. Thus, in some ways better than traditional statistics and non-mathematical human intuition.