Led a cutting-edge tutorial on the issues of transparency and interpretability of AI models. Both the research community and industry have been developing new techniques to render black-box Natural Language Processing (NLP) models more transparent and interpretable. This tutorial was based on my internship work in which I presented along with an interdisciplinary team comprising of human-computer interaction (HCI), and NLP researchers. The tutorial provided an introduction to explainable AI (XAI) and a review of the state-of-the-art for explainability research in NLP; and findings from a qualitative interview study with data scientists, AI engineers, technical strategists, and UX designers identifying practical challenges and AI model explainability concerns that arise in real-world deployments. http://www.aacl2020.org/program/tutorials/