A great talk on a very relevant subject: formalizing fairness in a drag-n-drop big data / ML world: Arvind Narayanan. Even though many people, including those working in the field, seem to think anything automated is therefore value free and fair, this presentation shows concretely how formalizing fairness is being investigated. For instance, you can go for outcome fairness or process fairness. Outsourcing decision making to computers means formalizing problems, and of course therefore also fairness, wether you leave it there as a hidden bias or not. Great example he gives is stereotype mirroring: if you search for images of CEOs you’ll find many men, and since most CEOs are men, this result is unbiased and correct, according to some. Or is it?
Such is the field of data ethics. A comprehensive course at fast.ai