You said > you can’t say that a particular person was misclassified, you are making a statement about the calibration of the algorithm But no one is arguing t

You said > you can’t say that a particular person was misclassified, you are making a statement about the calibration of the algorithm But no one is arguing that a particular person was misclassified; both the article and the study are about results in aggregate being skewed in some way. > They never define what the desired precision is, but their use of the terms "misclassify" and "false positive" makes sense only if one expects "high risk" to predict recidivism with 100% precision. They're not trying to find an absolute number for accuracy, they're comparing data for white and black defendants. Excerpt from the ProPublica article below: > Our analysis found that black defendants who did not recidivate over a two-year period were nearly twice as likely to be misclassified as higher risk compared to their white counterparts (45 percent vs. 23 percent). In this case, the equation for each race is (defendants who were labeled high risk but didn't recidivate / defendants who didn't recidivate). The accuracy in absolute terms isn't in question here; the claim they are making is that the algorithm mistakenly classifies black defendants as high risk at twice the rate it does white ones. Note that testing for true positives is different from testing for false positives, and both can be true simultaneously. If of 15 defendants, the algorithm predicts ten will recidivate, of whom six actually do, that gives 60% accuracy with ~45% of those who didn't misclassified as high risk. (I'm also not sure if you're making the semantic argument that "it just said recidivism was likely, not guaranteed, so even if they don't it's not technically wrong." That's a weird assertion to make; if every high-risk classification never recidivated but every low-risk one did, by that definition the algorithm still wouldn't be "wrong", but it would obviously be broken. Classifying a defendant as high risk is a prediction that they will recidivate, and we can judge the algorithm by whether it turned out to be true.) I'll also note that other replies to my original comment have been good rebuttals to the ProPublica study specifically, but what they're attempting to do — draw conclusions about false positives based on the "high risk" classifier — makes perfect sense.