Bayesian fairness
This addresses fairness in machine learning for decision-making systems, but it is incremental as it builds on existing fairness definitions like balance.
The paper tackles the problem of ensuring fairness in decision-making under uncertainty about probabilistic models, introducing Bayesian fairness as a solution and showing it leads to well-performing, fair decision rules even with high uncertainty.
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.