Re:Attacking discrimination with smarter machine learning
by yummyfajitas on Monday November 21, 2016 @02:02PM
(#13008109)
The problem is, when given access to a large number of classifiers, some of which have inevitably been affected by a pre-existing racial bias, a black box machine learning algorithm will likely become discriminatory as well if race is not in some way represented and equalized.
This is simply not true. Black box machine learning algorithms will have the tendency to correct bias in their inputs. Insofar as they do systematically deliver wrong answers, this is actually called "variance" and has no particular sign. It's just as likely to be biased in favor of $protected_class as against that class.
Also, you do know that Pro Publica's R script actually found no bias, right? The bias was actually in the selection of anecdotes in their article, which obscured the fact that their statistical analysis could not reject the null hypothesis.
This is simply not true. Black box machine learning algorithms will have the tendency to correct bias in their inputs. Insofar as they do systematically deliver wrong answers, this is actually called "variance" and has no particular sign. It's just as likely to be biased in favor of $protected_class as against that class.
https://www.chrisstucchio.com/blog/2016/alien_intelligences_...
Also, you do know that Pro Publica's R script actually found no bias, right? The bias was actually in the selection of anecdotes in their article, which obscured the fact that their statistical analysis could not reject the null hypothesis.
https://www.chrisstucchio.com/blog/2016/propublica_is_lying....