In recent years, we have seen gains in adoption of machine learning and artificial intelligence applications. However, continued adoption is being constrained by several limitations. The field of Explainable AI addresses one of the largest shortcomings of machine learning and deep learning algorithms today: the interpretability and explainability of models. As algorithms become more powerful and are better able to predict with better accuracy, it becomes increasingly important to understand how and why a prediction is made. Without interpretability and explainability, it would be difficult for us to trust the predictions of real-life applications of AI. Human-understandable explanations will encourage trust and continued adoption of machine learning systems as well as increasing system safety. As an emerging field, explainable AI will be vital for researchers and practitioners in the coming years.