6 ms·

It's far easier to hide an agenda behind verbiage and anecdotes. Go read the author's actual statistical analysis: https://github.com/propublica/compas-analysi

It's far easier to hide an agenda behind verbiage and anecdotes. Go read the author's actual statistical analysis: https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb https://github.com/propublica/compas-analysis/blob/master/Co... In the statistical analysis (unlike the verbiage) she is completely unable to hide the lack of bias and the accuracy of the algorithm, all of which are clearly on display in line [36]. In contrast, her verbiage somehow conveys the exact opposite impression.

pc86 10y ago
The data analysis you link to is by Jeff Larson, while the primary author of the article is Julia Angwin. Larson is still the second author so it is certainly a big question how he can present data showing no statistical correlation between race and score then have his name on an article saying the exact opposite that is clearly pushing an agenda. And as noted, one where the owners of the publication are also involved in a competing risk assessment product.
- yummyfajitas 10y ago
  It's not quite right that he shows no correlation between race and score. There is a strong correlation between race and score. This correlation is caused by the fact that blacks have a high recidivism rate (p = 4.52e-6). What the analysis shows is that once you know the predicted score of the algorithm, using race doesn't give you extra information. If the scores were biased then you could correct them by using racial information to undo the bias. For more detail on that last bit, read the "What if measurements are biased?" section of my blog post: https://www.chrisstucchio.com/blog/2016/alien_intelligences_and_discriminatory_algorithms.html https://www.chrisstucchio.com/blog/2016/alien_intelligences_... (The details differ a bit - I describe linear regression rather than cox models. But the basic idea is the same.)
zyxley 10y ago
Uh... it's all right there in your link, across several sections that analyze specific parts of the data. > Black defendants are 45% more likely than white defendants to receive a higher score correcting for the seriousness of their crime, previous arrests, and future criminal behavior. > Women are 19.4% more likely than men to get a higher score. > Most surprisingly, people under 25 are 2.5 times as likely to get a higher score as middle aged defendants. > The violent score overpredicts recidivism for black defendants by 77.3% compared to white defendants. > Defendands under 25 are 7.4 times as likely to get a higher score as middle aged defendants. > [U]nder COMPAS black defendants are 91% more likely to get a higher score and not go on to commit more crimes than white defendants after two year. > COMPAS scores misclassify white reoffenders as low risk at 70.4% more often than black reoffenders. > Black defendants are twice as likely to be false positives for a Higher violent score than white defendants. > White defendants are 63% more likely to get a lower score and commit another crime than Black defendants. Calling out one specific section that doesn't show bias doesn't magically exonerate the rest.
- yummyfajitas 10y ago
  None of these things are evidence of bias. The algorithm is biased if it's giving the wrong score due to race or redundantly encoded race. To show that the algorithm is biased, you need to show that (score, race) pairs are more predictive than (score, ) singletons. Line [36] and [46] both attempt to address this question. The only one of these which is statistically significant is "race_factorOther:score_factorHigh" in line [46]. The other things you bring up are interesting, but do not show bias. At best they show disparate impact which isn't remotely the same thing.
  - zyxley 10y ago
    > but do not show bias. At best they show disparate impact I have no interest in playing but-what-does-the-exact-dictionary-definition-say semantics games.
    - jdp23 10y ago
      The funny thing is that the dictionary definition supports your point, not yummyfajitas. bias: prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair.
      - belorn 10y ago
        If you are going to argue a single dictionary definition, you should immediately stop holding a mouse when using a computer. Rodents don't like being held for long times, be connected electrically to a machine, nor do they like being rubbed on a pad.
        zyxley 10y ago
        I think the obvious answer here is to genetically engineer a "computer mouse" that can mentally work complex algorithms, as supplied via a pet training clicker. Then, you'll be able to click your computer mouse to solve problems.

pc86 10y ago

The data analysis you link to is by Jeff Larson, while the primary author of the article is Julia Angwin. Larson is still the second author so it is certainly a big question how he can present data showing no statistical correlation between race and score then have his name on an article saying the exact opposite that is clearly pushing an agenda. And as noted, one where the owners of the publication are also involved in a competing risk assessment product.

yummyfajitas 10y ago
It's not quite right that he shows no correlation between race and score. There is a strong correlation between race and score. This correlation is caused by the fact that blacks have a high recidivism rate (p = 4.52e-6). What the analysis shows is that once you know the predicted score of the algorithm, using race doesn't give you extra information. If the scores were biased then you could correct them by using racial information to undo the bias. For more detail on that last bit, read the "What if measurements are biased?" section of my blog post: https://www.chrisstucchio.com/blog/2016/alien_intelligences_and_discriminatory_algorithms.html https://www.chrisstucchio.com/blog/2016/alien_intelligences_... (The details differ a bit - I describe linear regression rather than cox models. But the basic idea is the same.)

zyxley 10y ago

Uh... it's all right there in your link, across several sections that analyze specific parts of the data. > Black defendants are 45% more likely than white defendants to receive a higher score correcting for the seriousness of their crime, previous arrests, and future criminal behavior. > Women are 19.4% more likely than men to get a higher score. > Most surprisingly, people under 25 are 2.5 times as likely to get a higher score as middle aged defendants. > The violent score overpredicts recidivism for black defendants by 77.3% compared to white defendants. > Defendands under 25 are 7.4 times as likely to get a higher score as middle aged defendants. > [U]nder COMPAS black defendants are 91% more likely to get a higher score and not go on to commit more crimes than white defendants after two year. > COMPAS scores misclassify white reoffenders as low risk at 70.4% more often than black reoffenders. > Black defendants are twice as likely to be false positives for a Higher violent score than white defendants. > White defendants are 63% more likely to get a lower score and commit another crime than Black defendants. Calling out one specific section that doesn't show bias doesn't magically exonerate the rest.

yummyfajitas 10y ago
None of these things are evidence of bias. The algorithm is biased if it's giving the wrong score due to race or redundantly encoded race. To show that the algorithm is biased, you need to show that (score, race) pairs are more predictive than (score, ) singletons. Line [36] and [46] both attempt to address this question. The only one of these which is statistically significant is "race_factorOther:score_factorHigh" in line [46]. The other things you bring up are interesting, but do not show bias. At best they show disparate impact which isn't remotely the same thing.
- zyxley 10y ago
  > but do not show bias. At best they show disparate impact I have no interest in playing but-what-does-the-exact-dictionary-definition-say semantics games.
  - jdp23 10y ago
    The funny thing is that the dictionary definition supports your point, not yummyfajitas. bias: prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair.
    - belorn 10y ago
      If you are going to argue a single dictionary definition, you should immediately stop holding a mouse when using a computer. Rodents don't like being held for long times, be connected electrically to a machine, nor do they like being rubbed on a pad.
      - zyxley 10y ago
        I think the obvious answer here is to genetically engineer a "computer mouse" that can mentally work complex algorithms, as supplied via a pet training clicker. Then, you'll be able to click your computer mouse to solve problems.