Artificial neural nets can represent and classify many types of data but are
often tailored to particular applications -- e.g., for "fair" or "hierarchical"
classification. Once an architecture has been selected, it is often difficult
for humans to adjust models for a new task; for example, a hierarchical
classifier cannot be easily transformed into a fair classifier that shields a
protected field. Our contribution in this work is a new neural network
architecture, the concept subspace network (CSN), which generalizes existing
specialized classifiers to produce a unified model capable of learning a
spectrum of multi-concept relationships. We demonstrate that CSNs reproduce
state-of-the-art results in fair classification when enforcing concept
independence, may be transformed into hierarchical classifiers, or even
reconcile fairness and hierarchy within a single classifier. The CSN is
inspired by existing prototype-based classifiers that promote interpretability.

本文提出了一种新的神经网络结构 —— 概念子空间网络 (CSN)，能够将现有专门的分类器推广到产生一个统一模型，能够学习一系列的多概念关系。在实现概念独立性的情况下，我们证明了 CSN 在公平分类方面能够复现最先进的结果，可以被转变为分层分类器，甚至可以在单个分类器内调和公平性和层次性。