Equality of Opportunity in Supervised Learning · HackerPapers discussions

Equality of Opportunity in Supervised Learning — mentioned by 3wolf

Fri, 20 Aug 2021 19:17:02 GMT

Differences in lending rates between groups due to less data or confounding features is the motivating example in the oft-cited 'Equality of Opportunity in Supervised Learning'. Highly recommend it: https://arxiv.org/abs/1610.02413

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

Equality of Opportunity in Supervised Learning — mentioned by lcrmorin

Fri, 11 Dec 2020 21:38:45 GMT

Difficult to choose one this year (plenty of things happened + plenty of time to read things due to lock down). "Equality of Opportunity in Supervised Learning" (https://arxiv.org/abs/1610.02413) It explain the basic concept about fairness in ML. Very practical exemple in my domain knowledge that show the trade-off between fairness of an algo and overall performance (money). Really make you see what may go wrong with bias in ML. It shows, in my opinion, why we will have to regulate ML as corporation aren't really incentivized to deal with fairness. It also shows that there is different…

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

Equality of Opportunity in Supervised Learning — mentioned by lalaland1125

Sat, 22 Sep 2018 01:37:07 GMT

> Some people expect that fighting algorithmic racism is going to come with some sort of trade-off. Um, that's because we know it comes with trade-offs once you have the most optimal algorithm. See for instance https://arxiv.org/pdf/1610.02413.pdf. If your best performing algorithm is "racist" (for some definition of racist") you are mathematically forced to make tradeoffs if you want to eliminate that "racism". Of course, defining "racism" itself gets extremely tricky because many definitions of racism are mutually contradictory (https://arxiv.org/pdf/1609.05807.pdf).

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

Equality of Opportunity in Supervised Learning — mentioned by yorwba

Mon, 18 Jun 2018 03:55:09 GMT

> The goal should be equality of opportunity and evaluation rather than equality of outcome. In general, that will still require taking race into account. https://arxiv.org/abs/1610.02413 "Equality of Opportunity in Supervised Learning" discusses various fairness measures in the context of deciding loan applications based on credit scores. The issue is that you only know whether someone was qualified once they pay the loan back, and credit scores are not a perfect predictor of that, which on top of that is differently imperfect for different races. If you want equal opportunity (defined as…

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

Equality of Opportunity in Supervised Learning — mentioned by dlss

Thu, 19 Jan 2017 00:18:29 GMT

Google has actually released a paper on codifying what discrimination looks like for lending (https://arxiv.org/abs/1610.02413 + blog post: https://research.googleblog.com/2016/10/equality-of-opportun...). It can be trivially extended to discuss hiring. Basically a fair hiring process as defined by the above would be: P(hire | features) = P(hire | race=1, features) = P(hire | race=2, features) This may sound a bit silly or unnecessary, but unless this formula is specifically enforced, if a race were correlated with expected profit of a hire, a hiring algorithm might accidentally infer race…

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