Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark
Score
80论文首次大规模基准测试Mondrian CP与成本控制弃机在不平衡高风险决策中的性能，填补了该领域系统性实证空白。
实验设计严谨，覆盖15个数据集、7种模型、3种校准技术，3150次运行，统计显著性强，方法描述清晰。
直接给出数据相关的盈亏平衡阈值和部署指导，对信用评分、医疗、工业等领域具有直接应用价值。
论文2025年7月发布，关注当前AI部署中的核心痛点（不平衡+不确定性），时效性良好。
来源于arXiv预印本，尚未正式发表，但作者来自知名机构且方法可靠，信源中等偏上。
Base 89 × T2 Source weight ×0.9 = 80
Curation line 70Selected
Why selected
The paper features rigorous experimental design (3150 runs), highly significant results (p<1e-80), and directly addresses real-world deployment pain points, offering high information gain and practical value.
AI commentary
「This paper is a must-read for anyone deploying high-stakes AI systems. It reveals a critical flaw of standard conformal prediction in real-world imbalanced settings and provides a large-scale validated solution. For product managers and engineers in credit scoring, fraud detection, and healthcare, the combination of Mondrian CP with cost-controlled abstention is practically ready to use.」
Quick overview
High-stakes decision systems (credit scoring, fraud detection, healthcare, industrial safety) suffer from severe class imbalance and asymmetric error costs. Standard marginal conformal prediction (CP) provides valid overall coverage but severely under-covers rare, costly minority classes (as low as 0.5%). This comprehensive benchmark on 15 real-world imbalanced tabular datasets, 7 classifiers, 3 calibration methods, and 3150 runs shows that Mondrian CP restores valid minority-class coverage, improving average minority coverage by 61.7 percentage points (p<1e-80). Combining Mondrian CP with cost-controlled abstention significantly reduces expected decision cost under realistic human review budgets. The study quantifies dataset-specific break-even thresholds, offering practical guidance for deploying distribution-free, cost-aware uncertainty quantification in high-stakes decision support.
- Marginal conformal prediction severely under-covers minority classes in imbalanced high-stakes decisions, with coverage as low as 0.5%.
- Mondrian CP improves average minority-class coverage by 61.7 percentage points across 15 datasets.
- Combining Mondrian CP with cost-controlled abstention significantly reduces expected decision cost in human-in-the-loop settings.
- The paper provides dataset-specific break-even thresholds for practical deployment guidance.
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