Unverified50% confidenceFactExact time
Interpretability concerns whether the internal decision logic of a model can be understood by humans (for example, explaining how an output is produced through feature importance or attention weights); transparency concerns whether training data sources, model architecture, evaluation methods and even deployment conditions are disclosed.
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10/31/2026
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