Title: Bayesian fairness,
Abstract: 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 {\em
Bayesian fairness} as a suitable candidate for fair decision rules. Using
balance, a definition of fairness introduced by Kleinberg et al (2016), we show
how a Bayesian perspective can lead to well-performing, fair decision rules
even under high uncertainty.