Evaluation Cards for XAI Metrics

In the first week of May, a research team quietly posted a preprint proposing "Evaluation Cards for XAI Metrics" — a structured documentation framework not for AI models themselves, but for the tools we use to measure whether those models can explain their own decisions. That distinction matters more than it first appears. The explainability field has spent years producing metrics — faithfulness scores, stability measures, sensitivity indices — without producing any agreed standard for what a good metric actually looks like or what conditions it was designed to handle. This paper proposes borrowing from the model cards tradition, applying that same discipline of structured documentation to the metrics layer. The result, if it catches on, would be something like a nutrition label for XAI evaluation tools: here is what this metric measures, here are the assumptions baked in, here are the conditions under which it breaks down.

If confirmed and adopted, here is what this means. The practical problem in enterprise AI right now is not that explainability methods don't exist — they do, prolifically — it's that nobody agrees on how to evaluate them, which means auditors have no principled basis for trusting any particular explanation. A standardised evaluation card format gives compliance teams something concrete to point at. It gives regulators a vocabulary. It gives AI developers a forcing function: you can no longer ship a faithfulness metric without documenting its failure modes. The second-order effect is subtler but arguably more significant — once you have to document the assumptions of your XAI metric, you have to confront them, and that confrontation tends to be clarifying in ways that pure research pressure never quite achieves. The EU AI Act is arriving whether the field is ready or not. A framework like this could serve as the connective tissue between regulation's demand for accountability and engineering's need for something concrete to implement.

Watch for peer engagement: citations, commentary from XAI practitioners, or adoption signals from compliance-adjacent tooling. A second independent preprint building on this framework would shift the confidence picture considerably.

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