Fair Coresets via Optimal Transport

Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets. At the same time, machine learning is being increasingly applied to decision-making processes at a societal level, making it imperative for modelers to address inherent biases towards subgroups present in the data. Current approaches create fair synthetic representative samples by optimizing local properties relative to the original samples, but their effect on downstream learning processes …

biases cs.cy cs.lg data datasets decision distillation fair generate machine machine learning making popular processes samples scale set stat.ml subgroups tasks transport via