How is Machine Learning Different from Statistics and Why it Matters

As noted in the paper Derisking ML and AI by McKinsey [4], ML algorithms are typically far more complex than their statistical counterparts and often require design decisions to be made before the training process begins. The benefits of ML include superior performance and accuracy but their complexity leads to an added layer of challenge of interpretation, bias and compliance for ML. This is not just a technical problem though. The paper rightly points out that the degree of interpretability required is a policy choice. Feature Engineering -- ML is more complex because of the inherent difficulty of feature engineering -- that is, which features to use? How sound is each feature? Is it consistent with policy?