The words interpretable and explainable are closely related. However, in machine learning they refer to slightly different aspects of model behaviour. Choose the most appropriate definition below.

Question

The words interpretable and explainable are closely related.  However, in machine learning they refer to slightly different aspects of model behaviour. Choose the most appropriate definition below.

BlackTom AI · Accepted Answer

Here is the question restated: The words interpretable and explainable are closely related. However, in machine learning they refer to slightly different aspects of model behaviour. Choose the most appropriate definition below.

Option 1: 'Interpretability is the post-hoc ability of describing a machine learning model's decisions with approximations.' This statement describes post-hoc explanations, which are more characteristic of explainability approaches to black-box models rather than the core idea of interpretability tied to certain models themselves. The emphasis on approximations and post-hoc description makes this option more about explainability than about the inherent interpretability of a model.

Option 2: 'Interpretable models, also known as Glassbox models, make it possible to understand the cause of each effect underlying the model's decision. Interpretability is an inherit characteristic of the algorithm itself.' This option captures two key ideas: (a) interpretable or glassbox models are designed so that one can trace and understand why a decision was made, and (b) interpretability is presented as an intrinsic property of the model or its design. The phrase 'cause of each effect' aligns with tracing the reasoning path, though the wording 'inherit' is a minor linguistic slip; the essence is that interpretability is tied to the model and its transparency.

Option 3: 'Explainable models make it possible to understand the cause of each effect underlying the model's decision. Explainability is an inherit characteristic of the algorithm itself.' This choice swaps the terms: it asserts that explainable models inherently provide understanding, which targets explainability rather than interpretability. In practice, explainability often refers to post-hoc or external explanations, and asserting it as an inherent algorithmic property can blur the distinction that interpretable (glassbox) models aim for.

Option 4: 'Interpretability and Explainability have the same practical meaning in machine learning' This claim conflates two related but distinct concepts. Interpretability typically refers to the ability to understand the model's internal reasoning or structure, whereas explainability often involves external explanations or methods to interpret decisions. Treating them as identical obscures important nuances between model design and explanatory tools.

In summary, Option 1 emphasizes post-hoc description rather than intrinsic interpretability, Option 3 centers on explainability rather than interpretability, and Option 4 merges two related but not identical ideas. The remaining option aligns with the view that interpretable (glassbox) models reveal the causal reasoning behind decisions and treats interpretability as an inherent model property, despite a minor wording issue.

The words interpretable and explainable are closely related. However, in machine learning they refer to slightly different aspects of model behaviour. Choose the most appropriate definition below. Single choice

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