Be good (and don't be evil): how to audit your work for fairness and inclusion


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There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is likely reproducing or even amplifying existing prejudices and inequalities. Even when an analyst wants to pursue fairness and accuracy, it is easy to unintentionally create discriminatory code. I will discuss how to be good and avoid being part of the problem.

Editors Note:

I am looking for editors/curators to help with branches of the tree. Please send me an email  if you are interested.