为排序模型设计可控制的拒答机制，提升高风险场景下的决策安全性。
Bounded-Abstention Pairwise Learning to Rank
- 基于条件风险阈值决定是否拒答，实现安全可控的排序决策
- 理论证明最优拒答策略，并在多个数据集上验证效果
- 适用于医疗、教育等高风险领域的排序系统，适合关注安全性的研究者
排序系统在医疗、教育和就业等高风险领域影响重大，其经济与社会后果显著，因此必须集成安全机制。其中，拒答机制允许算法在不确定或置信度低时将决策权交由人工专家。尽管拒答已在分类任务中被广泛研究，但在其他机器学习范式中的应用仍不充分。本文提出一种新型的成对学习排序中的拒答方法，基于排名器的条件风险阈值：当估计风险超过预设阈值时，系统选择拒答。本文贡献包括：最优拒答策略的理论刻画、无需依赖特定模型的即插即用算法，以及在多个数据集上的全面实证评估，证明了该方法的有效性。
原文摘要 · Abstract (English)
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 abstention, which enables algorithmic decision-making systems to defer uncertain or low-confidence decisions to human experts. While abstention has 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 evaluation across multiple datasets, demonstrating the effectiveness of our approach.
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