  Considerable research effort has been guided towards algorithmic fairness but
real-world adoption of bias reduction techniques is still scarce. Existing
methods are either metric- or model-specific, require access to sensitive
attributes at inference time, or carry high development and deployment costs.
This work explores, in the context of a real-world fraud detection application,
the unfairness that emerges from traditional ML model development, and how to
mitigate it with a simple and easily deployed intervention: fairness-aware
hyperparameter optimization (HO). We propose and evaluate fairness-aware
variants of three popular HO algorithms: Fair Random Search, Fair TPE, and
Fairband. Our method enables practitioners to adapt pre-existing business
operations to accommodate fairness objectives in a frictionless way and with
controllable fairness-accuracy trade-offs. Additionally, it can be coupled with
existing bias reduction techniques to tune their hyperparameters. We validate
our approach on a real-world bank account opening fraud use case, as well as on
three datasets from the fairness literature. Results show that, without extra
training cost, it is feasible to find models with 111% average fairness
increase and just 6% decrease in predictive accuracy, when compared to standard
fairness-blind HO.
