Richard Nock
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A landmark negative result of Long and Servedio established a worst-case spectacular failure of a supervised learning trio (loss, algorithm, model) otherwise praised for its high precision machinery. Hundreds of papers followed up on the two suspected culprits: the loss (for being convex) and/or the algorithm (for fitting a classical boosting blueprint). Here, we call to the half-century+ founding theory of losses for class probability estimation (properness), an extension of Long and Servedio's results and a new general boosting algorithm to demonstrate that the real culprit in their specific context was in fact the (linear) model class. We advocate for a more general stanpoint on the problem as we argue that the source of the negative result lies in the dark side of a pervasive -- and otherwise prized -- aspect of ML: \textit{parameterisation}.
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Fair Wrapping for Black-box Predictions
Alexander Soen
Sanmi Koyejo
Nyalleng Moorosi
Ke Sun
Lexing Xie
NeurIPS (2022)
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We introduce a new family of techniques to post-process an accurate black box posterior and reduce its bias, born out of the recent analysis of improper loss functions whose optimisation can correct any \textit{twist} in prediction, unfairness being treated as one. Post-processing involves learning a function we define as an $\alpha$-tree for the correction, for which we provide two generic boosting compliant training algorithms. We show that our correction has appealing properties in terms of composition of corrections, generalization, interpretability and divergence to the black box. We exemplify the use of our technique for fairness compliance in three models: conditional value at risk, equality of opportunity and statistical parity and provide experiments on several readily available domains.
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