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).

本文结合度量学习和监督学习的思想，使用人类提供的三元组判断来学习人类兼容的决策重心表示，该表示与人类知觉更好地匹配，使人类能够更准确地进行预测，并在多个分类任务中通过合成数据和人类实验表明其有效性，从而获得显著提高的人类决策准确度（对蝴蝶与飞蛾分类增加了 17.8％，对肺炎分类增加了 13.2％）。