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The Utility of Interpretability — Emmanuel Amiesen

Latent Space: The AI Engineer Podcast

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Navigating AI Interpretability

This chapter examines the critical relationship between AI model interpretability and their development, emphasizing how understanding a model's inner workings can enhance performance and reduce biases. The discussion includes the limitations of current interpretability methods and advocates for designing easier-to-understand models from the outset. Additionally, it highlights the necessity of comprehending model behaviors to ensure safety and effectiveness as AI is increasingly integrated into various applications.

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