Data Science Principles for Interpretable and Explainable AI

Interpretability requires matching explanations to audience needs: The paper emphasizes that there is no one-size-fits-all approach to making AI transparent—different stakeholders (regulators, end-users, developers) need different types of explanations tailored to their specific goals and level of technical expertise.

"Glass box" models are fundamentally different from "explainable" ones: The paper introduces precise vocabulary distinguishing between inherently transparent models (glass box) and complex models made understandable after the fact (explainable), highlighting that these require different approaches to responsible deployment.

AI is often deployed without understanding its impacts: The authors note that despite AI's expanded capabilities across many domains, models are frequently put into use without fully assessing potential risks or consequences, underscoring a critical gap in responsible AI practice.

Classical design principles apply to AI systems: The paper connects interpretability to established concepts like parsimony (simplicity) and "gulfs of interaction" from design theory, suggesting that responsible AI benefits from applying time-tested principles beyond just technical solutions.

Interpretability itself needs objective evaluation criteria: Rather than treating explainability as subjective, the paper reviews methods for objectively assessing whether interpretability approaches actually work, emphasizing that transparency claims should be measurable and verifiable.