We Need Explanation Cards to Connect Explanation Algorithms to the Real World

2026-06-15 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Algorithmic explanations often fail to provide clear understanding of opaque decisions, primarily because their meaning is frequently counter-intuitive, requiring expert interpretation, and popular algorithms can be uninformative for complex functions. To address this, "Explanation Cards for Explanation Algorithms" are proposed. These cards augment standard explanations with crucial complementary information on robustness and validity, alongside explicit interpretation instructions. This approach makes otherwise uninformative explanations practically useful and helps detect their limitations. By shifting the responsibility for clear interpretation from users to providers, Explanation Cards, demonstrated with counterfactual explanations and SHAP, offer a practical method to operationalize the EU AI Act's explainability provisions, enhancing real-world applicability of explanation algorithms.

Key takeaway

For AI Ethicists and Machine Learning Engineers developing explainable AI systems, integrating Explanation Cards into your XAI workflows is crucial. This approach ensures that stakeholders correctly interpret algorithmic explanations by providing explicit guidance on robustness, validity, and conclusions. It also helps meet EU AI Act explainability requirements, shifting the burden of correct interpretation from users to providers and fostering greater trust.

Key insights

Explanation Cards bridge the gap between perceived and actual meaning of algorithmic explanations by providing crucial context and interpretation guidance.

Principles

• Algorithmic explanations often require expert knowledge.
• Popular explanation algorithms can be uninformative.
• Providers must explicitly guide explanation interpretation.

Method

Explanation Cards augment standard explanations with complementary information on robustness and validity, plus clear instructions for interpretation, shifting responsibility from users to providers.

In practice

• Construct Explanation Cards for SHAP.
• Construct Explanation Cards for counterfactual explanations.
• Operationalize EU AI Act explainability.

Topics

• Explanation Cards
• Explainable AI
• Algorithmic Transparency
• EU AI Act
• SHAP
• Counterfactual Explanations

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.