<ul><li>The rapid evolution of machine learning has led to the widespread adoption of complex "black box" models.</li><li>Efforts are focused on explaining these models instead of developing ones that are inherently interpretable.</li><li>In this position paper, the imperative need for model interpretability is emphasized.</li><li>An experimental evaluation of hybrid learning methods that integrate symbolic knowledge into neural network predictors is provided.</li></ul>

Investigating the Duality of Interpretability and Explainability in Machine Learning

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