This is the second part of our series about Machine Learning interpretability. We want to describe LIME (Local Interpretable Model-Agnostic Explanations), a popular t
Unlike usual performance metrics, fairness, safety and transparency in machine learning models are much harder if not impossible to quantify. Here are some techniques (and examples) to provide interpretability, to make decision systems understandable not only for their creators, but also for their customers and users.
Machine learning has a great potential to improve data products and business processes. It is used to propose products and news articles that we might be interested i