Understandability and root cause explanations are crucial for AI systems, especially when outcomes are unexpected. Differentiating between explanations and interpretations helps clarify how decisions are made, even when algorithms lack transparency. Emphasizing interpretability allows users to grasp the reasoning behind AI results, fostering trust and reliability in these technologies.
