Chapter 3 Interpretability

It is difficult to (mathematically) define interpretability. A (non-mathematical) definition of interpretability that I like by Miller (2017)3 is: Interpretability is the degree to which a human can understand the cause of a decision. Another one is: Interpretability is the degree to which a human can consistently predict the model's result 4. The higher the interpretability of a machine learning model, the easier it is for someone to comprehend why certain decisions or predictions have been made. A model is better interpretable than another model if its decisions are easier for a human to comprehend than decisions from the other model. I will use both the terms interpretable and explainable interchangeably. Like Miller (2017), I think it makes sense to distinguish between the terms interpretability/explainability and explanation. I will use "explanation" for explanations of individual predictions. See the section about explanations to learn what we humans see as a good explanation.

Interpretable machine learning is a useful umbrella term that captures the "extraction of relevant knowledge from a machine-learning model concerning relationships either contained in data or learned by the model". 5


  1. Miller, Tim. "Explanation in artificial intelligence: Insights from the social sciences." arXiv Preprint arXiv:1706.07269. (2017).

  2. Kim, Been, Rajiv Khanna, and Oluwasanmi O. Koyejo. "Examples are not enough, learn to criticize! Criticism for interpretability." Advances in Neural Information Processing Systems (2016).

  3. Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. "Definitions, methods, and applications in interpretable machine learning." Proceedings of the National Academy of Sciences, 116(44), 22071-22080. (2019).