Fair prediction with disparate impact: A study of bias in recidivism prediction instruments · HackerPapers discussions

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments — mentioned by praxulus

Thu, 18 May 2017 23:49:19 GMT

Recidivism prediction systems will usually tell you that black people are more likely to get arrested/convicted again. They do so accurately, but also result in longer sentences for black people. https://arxiv.org/abs/1610.07524

Paper and discussion history. A mention is not an endorsement.

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments — mentioned by randomtask

Mon, 08 May 2017 10:07:49 GMT

I cannot believe that the paragraph you cite actually appears in a journal article. It's really rather unprofessional and silly. I don't want to end up defending the methods ProPublica have used as I am certainly not qualified to do that and have no skin in this game anyway. I posted here initially in response to a very general question about bias in models, and I'd rather not be drawn into a lengthy discussion about this specific piece. However, I do have one or two issues with the conclusions you seem to be drawing in your comment: > What I find remarkable is that in the ongoing coverage…

Paper and discussion history. A mention is not an endorsement.

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments — mentioned by danielvf

Fri, 30 Dec 2016 23:53:45 GMT

TLDR: There are two different intuitive definitions of predictive fairness. Given populations with different base crime rates, it is impossible to meet both fairness criteria. If you can survive the WP payroll, the clearest article I've found on this is here: https://www.washingtonpost.com/news/monkey-cage/wp/2016/10/1... The full paper is here: https://arxiv.org/pdf/1610.07524v1.pdf

Paper and discussion history. A mention is not an endorsement.