Picking an explainability technique

ML Model Explainability (sometimes referred to as Model Interpretability or ML Model Transparency) is a fundamental pillar of AI Quality. It is impossible to trust a machine learning model without understanding how and why it makes its decisions, and whether these decisions are justified. Peering into ML models is absolutely necessary before deploying them in the wild, where a poorly understood model can not only fail to achieve its objective, but also cause negative business or social impacts, or encounter regulatory trouble. Explainability is also an important backbone to other trustworthy ML pillars like fairness and stability. Yet "explainability" is often a broad and sometimes confusing concept.