A Foundational Study of Algorithmic Bias

A Data Science and Analytics Committee (DSAC) policy paper, A Foundational Study of Algorithmic Bias, examines the types of biases that can be unintentionally embedded within the algorithms that drive machine-learning and AI systems. The paper notes that at its core, actuarial work revolves around accurate risk quantification and fair pricing, so the potential for algorithmic bias continues to be a growing concern.