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This paper proposes a joint-distribution approach to fair representation learning with continuous sensitive attributes, using HSIC as a joint discrepancy to avoid conditional density estimation. It proves statistical efficiency gains over conditional-route methods and introduces the FRHSIC algorithm with comparable fairness-accuracy tradeoffs and faster training.
This paper derives lower bounds on how much a company can manipulate fairness metrics after an audit, showing that finite-budget fairness certification cannot fully eliminate post-audit manipulation.