Identifying Bias in AI using Simulation
This addresses bias issues in AI deployment, particularly for minority populations, but is incremental as it builds on existing simulation and bias detection methods.
The paper tackles the problem of bias in AI models by proposing a simulation-based framework to diagnose biases in ML classifiers, demonstrating its application in identifying demographic biases in commercial face detection APIs.
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).