Black-Box vs. Explainable AI: How to Reduce Business Risk and Infuse TransparencyA key challenge on the journey to Enterprise AI will be figuring out how to balance model performance and interpretability stemming from the difference between black-box and white-box models.
As organizations scale their data science, machine learning, and AI efforts, they are bound to reach this impasse, learning when to prioritize white-box models over black-box ones (because there is a time and a place for those) and how to infuse explainability along the way.
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In this ebook, we’ll describe the trade-offs between white-box and black-box models, break down what these concepts really mean, and highlight the rise of explainable AI as a powerful mitigating force that enables modelers, regulators, and laypeople to have more trust and confidence in their models.