Machine learning models are often a black box which is difficult for designers to explain - but is this lack of explainability a necessary feature, or is it holding us back from creating models that we really need? To discuss, we caught up with Cynthia Rudin at the 2024 Joint Statistical Meetings to hear about her approach to a better approach: building models to be interpretable from the ground up, rather than focusing on after the fact explainability.
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Interpretable AI: Stop Explaining Black Box Machine Learning Models - with Cynthia Rudin
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