A new classifier derived from determinism and statistical consistency requirements achieves zero consistency error by construction and outperforms baselines on Adult, COMPAS, and Bank Marketing datasets.
Canadian Journal of Statistics 34, 709–721
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A residual-adequacy architecture unifies interpretation, learning, and empathy as one constraint through accountable abstention driven by representational mismatch.
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Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions
A new classifier derived from determinism and statistical consistency requirements achieves zero consistency error by construction and outperforms baselines on Adult, COMPAS, and Bank Marketing datasets.