Interpretability and explainability can lead to more reliable ML

With machine learning on the rise, businesses are relying on machine learning models and algorithms to derive insights from data and make predictions. Serg Masís, data scientist and author of Interpretable Machine Learning with Python from Packt Publishing Ltd., believes that in order to know how and why those algorithms make predictions, they must be both interpretable and explainable. In this Q&A, Masís discusses these concepts, coined "interpretablility" and "explainability," and how they are more than just buzzwords or theory by explaining their value in real-world scenarios. Editor's note: The following interview was edited for length and clarity. What near-future trends in machine learning will emerge, and will they adhere to the advice in this book about interpretability and explainability?