Weak interpretability

Weak interpretability refers to a level of understanding or clarity regarding how a machine learning model makes its decisions, where the insights provided are limited or not fully grasped by humans. In contrast to strong interpretability—where models provide clear, understandable, and easily explainable reasoning for their outputs—weaker forms of interpretability may involve models that are complex or opaque, with only partial explanations available.