interpretin

Interpretin is a hypothetical framework used in discussions of explainable artificial intelligence (AI) to structure how model outputs are interpreted by humans. It is described as a modular approach that emphasizes aligning explanations with user needs, model behavior, and decision context, rather than producing a single one-size-fits-all explanation.

Origins of the concept are rooted in 2010s debates about transparency and accountability in AI. Proponents describe Interpretin as a pedagogical and analytical tool rather than a specific algorithm, intended to clarify how different explanatory components relate to underlying model reasoning. The term is commonly used in theoretical critiques and educational materials to illustrate best practices for communicating complex predictions.

Core ideas of Interpretin include interpretive protocols, fidelity measures, and user-centered explanation templates. Interpretin protocols specify the kinds of information that should be disclosed for a given audience, such as feature contributions, counterfactuals, or uncertainty estimates. Fidelity measures assess how faithfully the explanation reflects the model’s actual logic and limitations. Explanation templates provide structured formats that normalize the presentation of information, making explanations more comparable across models and applications.

Applications of Interpretin are often discussed in the context of model auditing, regulatory compliance, product design, and public communication. By offering a framework for evaluating usefulness, accuracy, and privacy implications, Interpretin aims to support more responsible deployment of AI systems.

Criticism focuses on the abstract nature of the framework, potential overhead in generating explanations, and the risk of explanations shaping user perception rather than accurately reflecting model behavior. See also explainable AI, interpretable machine learning, and post-hoc explanations.