Notions of fair machine learning that seek to control various kinds of error across protected groups generally are cast as constrained optimization problems over a fixed model class. For such problems, tradeoffs arise: asking for various kinds of technical fairness requires compromising on overall error, and adding more protected groups increases error rates across all groups. Our goal is to break though such accuracy-fairness tradeoffs.

We develop a simple algorithmic framework that allows us to deploy models and then revise them dynamically when groups are discovered on which the error rate is suboptimal. Protected groups don't need to be pre-specified: At any point, if it is discovered that there is some group on which our current model performs substantially worse than optimally, then there is a simple update operation that improves the error on that group without increasing either overall error or the error on previously identified groups. We do not restrict the complexity of the groups that can be identified, and they can intersect in arbitrary ways. The key insight that allows us to break through the tradeoff barrier is to dynamically expand the model class as new groups are identified. The result is provably fast convergence to a model that can't be distinguished from the Bayes optimal predictor, at least by those tasked with finding high error groups.

We explore two instantiations of this framework: as a "bias bug bounty" design in which external auditors are invited to discover groups on which our current model's error is suboptimal, and as an algorithmic paradigm in which the discovery of groups on which the error is suboptimal is posed as an optimization problem. In the bias bounty case, when we say that a model cannot be distinguished from Bayes optimal, we mean by any participant in the bounty program. We provide both theoretical analysis and experimental validation.