Learning Human-Compatible Representations for Case-Based Decision Support. (arXiv:2303.04809v1 [cs.LG])

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).