data quality assurance teams

Abstract: Review-oriented workflows have become the dominant approach to AI-assisted document review in electronic discovery (eDiscovery). How to validate the effectiveness of review-oriented workflows has, however, remained a confusing and controversial topic in technology-assisted review (TAR) in the law. We examine the sources of this controversy, and propose a simple and practical evaluation approach for review-oriented workflows. Our Delayed Countdown method is easily incorporated into existing review-oriented workflows and software, is compatible with arbitrary stopping rules, and produces valid frequentist confidence intervals on effectiveness. We explain the conceptual foundations of the method, provide example computations, discuss the handling of logically impossible recall values, and suggest future work on decreasing review costs.

Abstract: Conformal test martingales (CTMs) are e-processes for sequentially testing exchangeability via p-values produced by a conformity function $A$. However, any distribution shift that preserves the push-forward $P\circ A^{-1}$ leaves the p-value distribution invariant, rendering it invisible to every CTM based on $A$. We formalise this blind spot through the \emph{$A$-equivalence class} $[P]_A$ and show that, for any target distribution $T$, there is a unique closest element $Q^\ast\in[P]_A$ in KL divergence---the \emph{$A$-cryptic I-projection}---with explicit density $q^\ast(z) = t(z)\,p_S(A(z))/t_S(A(z))$ and a Pythagorean decomposition into detectable and cryptic components. When $A$ is the Neyman-Pearson likelihood ratio, the cryptic component vanishes, connecting our result to optimal e-value design. We demonstrate the theory on high-dimensional image data by empirically constructing this I-projection. By conditionally resampling EMNIST letters to identically match the conformity scores of MNIST digits, we demonstrate that a distribution can be shifted to a disjoint support while leaving the test martingale mathematically blinded.