Research paper on feature attribution methods for machine learning explainability. Addresses rigorous, principled approaches to interpreting model decisions by analyzing how individual features contribute to predictions.
Research
Towards Rigorous Explainability by Feature Attribution
Researchers formalize feature attribution methods with mathematical rigor, establishing principled foundations for reproducible model explainability.
Monday, April 20, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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