What is the impact of scaling the number of pre-training causal models on the fairness-accuracy trade-off in T

Abstract

Abstract: Training machine learning models for fair decisions faces two key challenges: The emph\fairness-accuracy trade-off\ results from enforcing fairness which weakens its predictive performance in contrast to an unconstrained model. The incompatibility of different fairness metrics poses another trade-off – also known as the emph\impossibility theorem\. Recent work identifies the bias within the observed data as a possible root cause and shows that fairness and predictive performance are in fact in accord when predictive performance is measured on unbiased data. We offer a causal explanation for

Research Question

What is the impact of scaling the number of pre-training causal models on the fairness-accuracy trade-off in TabPFN, evaluated using disparity metrics and accuracy scores on biased tabular benchmarks?

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