Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, with more data available than ever before, it has become increasingly important to explain automated predictions. Generally, users find it difficult to understand the underlying computational processes and interact with the models, especially when the models fail to generate the outcomes or explanations, or both, correctly. This problem highlights the growing need for users to better understand the models' inner workings and gain control over their actions. This dissertation focuses on two fundamental challenges of addressing this need. The first involves explanation generation: inferring high-quality explanations from text documents in a scalable and data-driven manner. The second challenge consists in making explanations actionable, and we refer to it as critiquing. This dissertation examines two important applications in natural language processing and recommendation tasks. Overall, we demonstrate that interpretability does not come at the cost of reduced performance in two consequential applications. Our framework is applicable to other fields as well. This dissertation presents an effective means of closing the gap between promise and practice in artificial intelligence.

翻译：人工智能与机器学习算法已变得无处不在。尽管它们带来了广泛益处，但由于缺乏可解释性（尤其是在处理文本数据时），其在关键决策领域的应用受到限制。此外，随着可用数据量空前增长，解释自动化预测的需求日益重要。通常情况下，用户难以理解底层计算过程并与模型交互，尤其在模型未能正确生成结果或解释（或两者均未正确生成）时。这一问题凸显了用户深入理解模型内部机制并对其行为进行控制的迫切需求。本论文聚焦于应对这一需求的两项核心挑战：其一是解释生成——以可扩展且数据驱动的方式从文本文档中推断高质量解释；其二是使解释具备可操作性，我们称之为批评机制。本文探讨了自然语言处理与推荐任务中的两个重要应用场景。总体而言，我们证明在两个关键应用中，可解释性并不会以牺牲性能为代价。我们的框架同样适用于其他领域。本论文为弥合人工智能领域理论与实践之间的鸿沟提供了有效途径。