Missingness Bias Calibration in Feature Attribution Explanations

ArXi:2603.04831v2 Announce Type: replace 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 re