What's Fair?

Data, algorithms, and systems have biases embedded within them reflecting designers' explicit and implicit choices, historical biases, and societal priorities. They form, literally and inexorably, a codification of values. \"Unfairness\" of algorithms -- for tasks ranging from advertising to recidivism prediction -- has attracted considerable attention in the popular press. The talk will discuss the nascent mathematically rigorous study of fairness in classification and scoring.