Interpretable machine learning refers to models that are designed to be transparent and understandable, allowing users to see how decisions are made. This is crucial, especially in high-stakes situations like healthcare, where understanding the reasoning behind a model's prediction can impact patient care. Cynthia Rudin advocates for these models to replace the opaque 'black boxes' that are often used, which can lead to mistrust and errors.

Transparency in AI models is essential because it builds trust and allows for better troubleshooting. For instance, if a model makes a prediction about a patient's health, understanding how that prediction was reached is vital for ensuring its relevance and accuracy. Rudin emphasizes that without transparency, it can be difficult to identify flaws or biases in the model, which could lead to serious consequences.

Case-based reasoning involves making predictions based on similarities to past cases, similar to how a doctor might diagnose a patient by comparing their symptoms to previous experiences. Rudin's work shows that this approach can be applied to complex AI models, such as those used in radiology, allowing them to remain interpretable without sacrificing accuracy. This method has been successfully adapted for various medical applications, demonstrating its versatility and effectiveness.