One of the largest barriers to widespread machine learning (ML) adoption is its lack of explainability. Most ML models are not inherently explainable on a local level, meaning that the model cannot provide any reasoning to support individual decisions. The academic and private sectors are very active in developing solutions to the explainability issue, and this Leadership Brief introduces the main methods that make AI explainable.
