Machine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI

It doesn't take a data scientist to work out that the machine and deep learning algorithms built into automation and artificial intelligence systems lack transparency. It also doesn't take a great deal of detective work to see that many of these systems contain an imprint of the unconscious biases of the engineers that helped to develop them. Arguably, in the midst of what The Economist termed a techlash, this lack of transparency has only (ironically) become more visible. While many of the incidents that have contributed towards the techlash are as much issues caused by a mixture of corporate self-interest and an alarming absence of governance and accountability, there's no escaping the fact that the practice of data science and machine learning engineering naturally find their way hooked onto some of the year's biggest business and politics stories. It's in this context that the concepts of explainability and interpretability have taken on new urgency.