The Data Science and Analytics Committee (DSAC) released Measuring Statistical Bias in Data Using Entropy, a policy paper exploring how entropy can be used to quantify statistical bias and diversity in data. Tracing entropy’s data science origins in information theory, the paper highlights how it applies it across domains, including actuarial science, finance, and machine learning. The paper also provides examples of how entropy can support bias detection, decision tree modeling, and data balancing, offering actuaries a computational tool to assess data fairness and structure.