The Importance of Explainable AI and Interpretable AI

In this episode of the AI Today podcast, the hosts discuss the importance of explainable AI and interpretable AI. They highlight how relying on black box technology, like deep learning, can be dangerous due to the lack of transparency and understanding of how specific inputs result in specific outputs. They emphasize the need for trust, accountability, and explanation in AI systems to avoid eroding trust and ensure human oversight. While not all machine learning approaches are inherently explainable, some algorithms like decision trees and ensemble methods provide more explainability than others. The hosts also introduce the concept of interpretable AI, which aims to provide a reasonable understanding of the main factors contributing to a decision, even if the underlying algorithm lacks explainability.