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cs.LG2026
Reliability, Faithfulness, and the Limits of Post-hoc Explanations of Opaque Scientific Models
Nick Oh, Helen Jin
Post-hoc explanation methods are routinely used to interpret scientific machine learning models, with the deliverable understood to be insight into the phenomenon the model has bee…
cs.LG2025
In Defence of Post-hoc Explainability
Nick Oh
This position paper defends post-hoc explainability methods as legitimate tools for scientific knowledge production in machine learning. Addressing criticism of these methods' reli…