We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of Bayesian fairness as a suitable candidate for fair decision rules. Using balance, a definition of fairness introduced in [Kleinberg, Mullainathan, and Raghavan, 2016], we show how a Bayesian perspective can lead to well-performing and fair decision rules even under high uncertainty.