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This position paper outlines five major shortcomings of existing faithfulness metrics for explainable AI feature importance and proposes four research avenues to improve their reliability, arguing that XAI explanations currently lack rigorous anchors for quantifying fidelity to the underlying model.
This paper identifies a blind spot in reference-free faithfulness metrics: they only measure precision (whether claims are supported) but not recall (coverage of relevant facts). The authors introduce a complete-oracle evaluation using Formula 1 telemetry and weather data, showing that high-precision models often have poor coverage, and propose a combined metric.