From the course: Data-Centric AI: Best Practices, Responsible AI, and More
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Techniques for understanding and interpreting ML models
From the course: Data-Centric AI: Best Practices, Responsible AI, and More
Techniques for understanding and interpreting ML models
- [Presenter] Let me talk about the difference between explainability and interpretability. Explainability refers to being able to understand why an AI model makes specific predictions or decisions. It enables us to question the model when things go wrong and audit the model for issues like bias. Explainability is crucial for debugging and improving the model performance. In contrast, interpretability involves building AI models whose logic and mechanisms are human-comprehensive. The model itself is designed to be inherently understandable, not just its outputs. Interpretability is critical for building trust in the AI systems. Humans are more likely to trust the models that they're able to interpret. Explainability involves generating explanations of the AI system behavior for non-technical stakeholders in an accessible manner. It ensures transparency and accountability, especially in high stake applications like…
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