AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion cover image

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

AI Today Podcast: Trustworthy AI Series: Explainable & Interpretable AI

Oct 20, 2023
The podcast explores the importance of explainable and interpretable AI, including the need for verifiable explanations, algorithmic transparency, and debugging AI systems. It discusses the significance of understanding root cause explanations and the challenges in achieving explainability. The chapter also highlights the difference between black box and white box algorithms and the importance of choosing the right algorithmic approach. It concludes by discussing trustworthy AI resources, upcoming interviews, and listener feedback.
18:51

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Quick takeaways

  • Explainable and interpretable AI is essential for building trustworthy AI systems and establishing trust.
  • Choosing the right algorithmic approaches, such as decision trees and linear models, can enhance explainability and trust in AI systems.

Deep dives

Importance of Explainable and Interpretable AI

Explainable and interpretable AI is a crucial aspect of trustworthy AI. Without understandability, trust cannot be established in AI systems. Verifiable explanations for how machine learning systems make decisions allow humans to be in the loop and hold someone accountable. While not all machine learning approaches are inherently explainable, it is essential to prioritize approaches that provide explanations on how outcomes were arrived from input data. This can include the use of more explainable algorithms like decision trees and linear models.

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