Unsupervised learning allows us to leverage unlabelled data, which has become abundantly available, and to create embeddings that are usable on a variety of downstream tasks. However, the typical lack of interpretability of unsupervised representation learning has become a limiting factor with regard to recent transparent-AI regulations. In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis. We also introduce additional metrics addressing the lack of formalism and metrics in the understudied area of unsupervised-representation learning interpretability. Our results are supported by an experimental study applied to both graph-level and node-level tasks and show that interpretable embeddings provide state-of-the-art performance on subsequent downstream tasks.

翻译：无监督学习使我们能够利用大量可用的未标注数据，创建可用于多种下游任务的嵌入表示。然而，无监督表示学习通常缺乏可解释性，这已成为近期透明人工智能法规下的一个限制因素。本文研究图表示学习，并证明能够学习并利用保持语义的数据增强来生成解释。我们提出的框架名为INGENIOUS，可创建本质可解释的嵌入，从而消除代价高昂的额外事后分析需求。我们还引入了新的评估指标，以解决无监督表示学习可解释性这一研究不足领域中形式化方法和度量标准的缺失问题。我们的结果得到了应用于图级和节点级任务的实验研究的支持，表明可解释嵌入在后续下游任务中提供了最先进的性能。