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KAIST AI
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Subgroup fairness in clustering can be achieved efficiently without sacrificing performance, thanks to a novel covariance-based approach that reveals the hidden complexities of fairness gaps.
Fair clustering just got a whole lot faster: FMC achieves fairness without the computational bottleneck of sample-dependent parameters.