Evaluation Cards for XAI Metrics

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

Rokas Gipiškis and Olga Kurasova introduce the XAI Evaluation Card, a documentation template designed to standardize the reporting and evaluation of explainable AI (XAI) methods. This initiative addresses significant challenges in XAI research, including inconsistent metric definitions, incomplete reporting practices, and a general lack of validation against common baselines. Analogous to model cards, the proposed XAI Evaluation Card requires explicit declaration of critical aspects for any new XAI evaluation metric. These include target properties, grounding levels, underlying metric assumptions, validation evidence, potential gaming risks, and identified failure cases. Adopting this template as a community standard is argued to reduce evaluation fragmentation, facilitate meta-analysis, and enhance accountability within the XAI research domain.

Key takeaway

For research scientists developing or evaluating explainable AI (XAI) methods, adopting the XAI Evaluation Card is crucial. This template provides a standardized framework to document your metric's target properties, assumptions, and validation evidence, directly addressing current evaluation fragmentation. By consistently using these cards, you can enhance the transparency and accountability of your XAI research, making your contributions more comparable and robust for the broader community.

Key insights

The XAI Evaluation Card standardizes reporting for XAI metrics, addressing fragmentation and improving accountability.

Principles

• Standardized reporting improves XAI evaluation.
• Transparency in metrics reduces fragmentation.
• Documenting assumptions prevents gaming risks.

Method

The XAI Evaluation Card template documents XAI evaluation metrics by detailing target properties, grounding levels, assumptions, validation evidence, gaming risks, and failure cases.

In practice

• Use the card for new XAI metric proposals.
• Apply card structure to existing metric reviews.
• Compare metrics using standardized card data.

Topics

• Explainable AI
• XAI Evaluation
• Metric Standardization
• Evaluation Cards
• Research Accountability
• Meta-analysis

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.