AI Today Podcast: Ethical & Responsible AI Series: Explainable and Interpretable AI

Techniques

Interpretability techniques are crucial for understanding AI models, allowing users to grasp how decisions are made. Ron Schmelzer explains that interpretability involves identifying key factors influencing AI decisions, such as data elements that impact model accuracy 1. This understanding aids in debugging and improving AI systems. He illustrates this with an analogy of a GPS system, where the computer's literal interpretation of instructions can lead to unexpected outcomes, highlighting the need for human oversight 1.

   

Roles

Interpretability and explainability, though related, serve distinct roles in AI understanding. Ron Schmelzer describes explainability as the model's ability to articulate how it reached a decision, akin to a child explaining their actions 2. He notes that while neural networks pose challenges, methods are being developed to make them less of a 'black box' 2. Interpretability, on the other hand, focuses on understanding the inputs and processes that lead to an outcome, emphasizing the importance of transparency in AI systems 3.