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The mainstream industry approaches to addressing interpretability issues include using gradient boosting trees or hybrid rule-based models instead of pure neural networks, appending confidence intervals and salient feature annotations to neural network outputs, and adopting a two-tier architecture of AI suggestions plus human confirmation.
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9/17/2026
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