AI RESEARCH
First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution
arXiv CS.AI
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ArXi:2603.22346v1 Announce Type: cross We isolate and empirically characterize first-mover bias -- a path-dependent concentration of feature importance caused by sequential residual fitting in gradient boosting -- as a specific mechanistic cause of the well-known instability of SHAP-based feature rankings under multicollinearity.