  Machine learned models exhibit bias, often because the datasets used to train
them are biased. This presents a serious problem for the deployment of such
technology, as the resulting models might perform poorly on populations that
are minorities within the training set and ultimately present higher risks to
them. We propose to use high-fidelity computer simulations to interrogate and
diagnose biases within ML classifiers. We present a framework that leverages
Bayesian parameter search to efficiently characterize the high dimensional
feature space and more quickly identify weakness in performance. We apply our
approach to an example domain, face detection, and show that it can be used to
help identify demographic biases in commercial face application programming
interfaces (APIs).
