The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this “black box” after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network’s architecture — before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.
Originally from KDnuggets https://ift.tt/3pU5HAf
source https://365datascience.weebly.com/the-best-data-science-blog-2020/deep-learning-doesnt-need-to-be-a-black-box
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