2026
Missingness Bias Calibration in Feature Attribution Explanations
ICLR 2026poster
Popular explanation methods often produce unreliable feature importance scores due to "missingness bias", a systematic distortion that arises when models are probed with ablated, out-of-distribution inputs. Existing solutions treat this as a deep representational flaw that requires expensive retrain…