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Algorithmic interpretability is necessary to build trust, ensure fairness,and track accountability. However, there is no existing formal measurementmethod for algorithmic interpretability. In this work, we build uponprogramming language theory and cognitive load theory to develop a frameworkfor measuring algorithmic interpretability. The proposed measurement frameworkreflects the process of a human learning an algorithm. We show that themeasurement framework and the resulting cognitive complexity score have thefollowing desirable properties - universality, computability, uniqueness, andmonotonicity. We illustrate the measurement framework through a toy example,describe the framework and its conceptual underpinnings, and demonstrate thebenefits of the framework, in particular for managers considering tradeoffswhen selecting algorithms.

Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score