Explainable Machine Learning (XAI) refers to efforts to make sure that artificial intelligence programs are transparent in their purposes and how they work. This is understandable because a lot of SOTA (State of the Art) models are black boxes which are difficult to interpret or explain despite their top-notch predictive power and performance. For many organizations and corporations, several percentage increase in classification accuracy may not be as important as answers to questions like "how does feature A affect the outcome?" This is why XAI has been receiving more spotlight as it greatly aids decision making and performing causal inference. In the next series of posts, I will cover various XAI methodologies that are in wide use nowadays in the Data Science community.
Dec-25-2021, 21:05:37 GMT