Interpretability
Degree to which humans can understand AI decisions.
Definitions (2)
The property of AI systems that allows stakeholders to perceive and understand how inputs and internal processes lead to specific outputs or decisions. Interpretability supports meaningful explanations, facilitates error analysis, and enables informed human oversight and trust.
Interpretability, often closely related to explainability, denotes the extent to which a human can grasp the internal mechanics of an AI system, including how inputs lead to outputs and the significance of various model parameters. It focuses on the intrinsic transparency of a model's operations, allowing for a deeper understanding of its decision-making logic.
Related Terms
Explicability
Making AI outputs interpretable to stakeholders....
Algorithmic Explainability
Ability to understand how an AI system reaches decisions....
Explainability/interpretability
Extent to which a model's operation and outputs can be meaningfully described....
Explicability of AI
Requirement that AI decisions can be interpreted and explained....
model transparency
Clarity about a model's operation, inputs, outputs and limitations....