AI RESEARCH
Explainable AI Through a Democratic Lens: DhondtXAI for D'Hondt-Projected Feature Attribution
arXiv CS.AI
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ArXi:2411.05196v3 Announce Type: replace This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI. Instead of model-native feature importance or SHAP values, DhondtXAI computes background-interventional removal effects, separates positive and negative evidence, forms optional feature alliances, applies optional thresholds, allocates seats via the D'Hondt rule, and projects onto the local model-output difference. Completeness is preserved by construction, with the projection residual ratio reported as a diagnostic.