Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety 事件

Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety arXiv:2606.05461v1 Announce Type: new Abstract: Safety standards for ML-based autonomous driving specify the kind of evidence an assurance case must contain (directed cause-and-effect chains, quantified interventional effects, named root-cause variables), yet the XAI literature is organised by output type and technique family (saliency maps, feature attribution, counterfactuals, causal graphs,