Foulds, JamesIslam, RashidulKeya, Kamrun NaherPan, Shimei2025-01-082025-01-082020-01Foulds, James R., Rashidul Islam, Kamrun Naher Keya, and Shimei Pan. “Bayesian Modeling of Intersectional Fairness: The Variance of Bias.” In Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), 424–32. Proceedings. Society for Industrial and Applied Mathematics, 2020. https://doi.org/10.1137/1.9781611976236.48.https://doi.org/10.1137/1.9781611976236.48http://hdl.handle.net/11603/37199Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), Hilton Cincinnati Netherland Plaza, Cincinnati, USA, on May 7–9, 2020.Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability. Intersectionality theory therefore implies it is important that fairness in artificial intelligence systems be protected with regard to multi-dimensional protected attributes. However, the measurement of fairness becomes statistically challenging in the multi-dimensional setting due to data sparsity, which increases rapidly in the number of dimensions, and in the values per dimension. We present a Bayesian probabilistic modeling approach for the reliable, data-efficient estimation of fairness with multidimensional protected attributes, which we apply to two existing intersectional fairness metrics. Experimental results on census data and the COMPAS criminal justice recidivism dataset demonstrate the utility of our methodology, and show that Bayesian methods are valuable for the modeling and measurement of fairness in intersectional contexts.9 pagesen-US© 2024 Society for Industrial and Applied MathematicsText