Model Transparency Challenges
The discussion highlights the complexities of achieving transparency in deep learning models, especially when dealing with unstructured data like medical images. While interpretable models like regression are straightforward, the need for transparency in the development process is emphasized, ensuring clients are engaged and informed partners. The conversation also touches on the limitations of existing methods and the ongoing debate about the balance between interpretability and performance in advanced applications.In this clip
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655: AI ROI: How to get a profitable return on an AI-project investment — with Keith McCormick
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