As local governments increasingly experiment with predictive machine-learning models to allocate social services and welfare resources, there is a fierce debate regarding data biases and systemic equity. How can we as social scientists design better empirical frameworks to help policymakers audit these models for systemic bias before they are deployed on vulnerable populations?
