Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans. As a result, they have limited effectiveness in case-based decision support. In this work, we incorporate ideas from metric learning with supervised learning to examine the importance of alignment for effective decision support. In addition to instance-level labels, we use human-provided triplet judgments to learn human-compatible decision-focused representations. Using both synthetic data and human subject experiments in multiple classification tasks, we demonstrate that such representation is better aligned with human perception than representation solely optimized for classification. Human-compatible representations identify nearest neighbors that are perceived as more similar by humans and allow humans to make more accurate predictions, leading to substantial improvements in human decision accuracies (17.8% in butterfly vs. moth classification and 13.2% in pneumonia classification).

翻译：算法化案例决策支持通过提供示例帮助人类理解预测标签并辅助决策任务。尽管监督学习性能优异，但监督模型学到的表示可能与人类直觉不一致：模型视为相似的示例可能被人类认为不同。因此，这些表示在案例决策支持中的有效性有限。本研究将度量学习思想与监督学习相结合，探讨对齐性对有效决策支持的重要性。除实例级标签外，我们利用人类提供的三元组判断来学习聚焦决策的人类兼容表示。通过多个分类任务中的合成数据实验和人类主体实验，我们证明此类表示比仅优化分类的表示更符合人类感知。人类兼容表示能识别出被人类认为更相似的近邻，并帮助人类做出更准确的预测，使人类决策准确率显著提升（蝴蝶与蛾分类提升17.8%，肺炎分类提升13.2%）。