  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.
