Bounded-Abstention Pairwise Learning to Rank
This addresses the need for safer algorithmic decision-making in high-stakes domains like health and employment, though it is an incremental extension of abstention from classification to ranking tasks.
The paper tackles the problem of integrating safety mechanisms into ranking systems by introducing a novel abstention method for pairwise learning-to-rank tasks, where the system defers uncertain decisions based on a risk threshold, and demonstrates its effectiveness through empirical evaluations across multiple datasets.
Ranking systems influence decision-making in high-stakes domains like health, education, and employment, where they can have substantial economic and social impacts. This makes the integration of safety mechanisms essential. One such mechanism is $\textit{abstention}$, which enables algorithmic decision-making system to defer uncertain or low-confidence decisions to human experts. While abstention have been predominantly explored in the context of classification tasks, its application to other machine learning paradigms remains underexplored. In this paper, we introduce a novel method for abstention in pairwise learning-to-rank tasks. Our approach is based on thresholding the ranker's conditional risk: the system abstains from making a decision when the estimated risk exceeds a predefined threshold. Our contributions are threefold: a theoretical characterization of the optimal abstention strategy, a model-agnostic, plug-in algorithm for constructing abstaining ranking models, and a comprehensive empirical evaluations across multiple datasets, demonstrating the effectiveness of our approach.