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The propublica study has been shown to be a lie, see https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html https://www.chrisstucchio.com/blog/2016/pr

The propublica study has been shown to be a lie, see https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html https://www.chrisstucchio.com/blog/2016/propublica_is_lying.... Anyway all I'm claiming is that statistical methods are much more accurate than humans. Nothing there disputes that claim. If you want the most accurate predictions possible, you should use an algorithm. That article implies that humans are somehow fair or unbiased. That is a completely ridiculous claim that has been proven false many times. Human judges give ugly people twice the sentences of attractive people. Judges have been shown to give significantly harsher sentences just before lunch, when they are hungry. Not to mention all the classic biases against gender/race/political affiliation/etc. Studies have shown interviews are worse than useless at assessing how good someone will be as an employee. Instead employers are biased by how much they like the candidate. We should hardly expect traditional parole interviews to be any different. But almost no one cares about these results. Yet when an algorithm is shown to have a (not statistically significant) bias, people freak out. This, if anything, proves my point that algorithm aversion is a serious problem.

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.
xkxx 10y ago
> Human judges give ugly people twice the sentences of attractive people. Judges have been shown to give significantly harsher sentences just before lunch, when they are hungry. It would be nice if you could provide sources for these claims.
- jacquesm 10y ago
  It took only 10 seconds of googling to come up with this: https://www.scientificamerican.com/article/lunchtime-leniency/ https://www.scientificamerican.com/article/lunchtime-lenienc... http://www.villagevoice.com/news/study-ugly-people-more-likely-to-end-up-in-jail-6677702 http://www.villagevoice.com/news/study-ugly-people-more-like...
- CountHackulus 10y ago
  Here's the paper for the just before lunch claim: http://www.pnas.org/content/108/17/6889 http://www.pnas.org/content/108/17/6889
- soundwave106 10y ago
  The lunch statistic probably came from this study: http://www.economist.com/node/18557594 http://www.economist.com/node/18557594 http://www.pnas.org/content/108/17/6889 http://www.pnas.org/content/108/17/6889 (To be fair, the study size was small). There's a study on attractiveness and juror bias here (it's more complicated than just "ugly people get worse sentences" but some bias does show up for certain juror personality types): http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract
  - sjg007 10y ago
    If there is ever a dress for success time it is when you are in court.

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.

xkxx 10y ago

> Human judges give ugly people twice the sentences of attractive people. Judges have been shown to give significantly harsher sentences just before lunch, when they are hungry. It would be nice if you could provide sources for these claims.

jacquesm 10y ago
It took only 10 seconds of googling to come up with this: https://www.scientificamerican.com/article/lunchtime-leniency/ https://www.scientificamerican.com/article/lunchtime-lenienc... http://www.villagevoice.com/news/study-ugly-people-more-likely-to-end-up-in-jail-6677702 http://www.villagevoice.com/news/study-ugly-people-more-like...
CountHackulus 10y ago
Here's the paper for the just before lunch claim: http://www.pnas.org/content/108/17/6889 http://www.pnas.org/content/108/17/6889
soundwave106 10y ago
The lunch statistic probably came from this study: http://www.economist.com/node/18557594 http://www.economist.com/node/18557594 http://www.pnas.org/content/108/17/6889 http://www.pnas.org/content/108/17/6889 (To be fair, the study size was small). There's a study on attractiveness and juror bias here (it's more complicated than just "ugly people get worse sentences" but some bias does show up for certain juror personality types): http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract http://onlinelibrary.wiley.com/doi/10.1002/bsl.939/abstract
- sjg007 10y ago
  If there is ever a dress for success time it is when you are in court.