There is growing concern about fairness in algorithmic decision making: Is it treating different groups fairly? How can we make it fairer? And what do we even mean by fair? In this talk I will discuss some of our work on this topic, focusing on the setting of online decision making. For instance, a classic result states that given a collection of predictors, one can adaptively combine them to perform nearly as well as the best in hindsight (achieve “no regret”) without any stochastic assumptions. Can one extend this guarantee so that if the predictors are themselves fair, the overall combination is as well? I will discuss this and other issues.