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. The method is evaluated on synthetic additive and interaction tests, correlated-feature perturbations, operator and apportionment ablations, projection-mode comparisons, logit-scale checks, repeated split validation, paired deletion tests, and two healthcare datasets: Wisconsin Diagnostic Breast Cancer (CatBoost) and early-stage diabetes risk prediction (XGBoost). SHAP serves only as an external comparator with aligned settings. In additive synthetics, DhondtXAI exactly recovers ground-truth rankings; in multiplicative interactions, alliances reduce the mean projection residual from 0.2527 to 0.0001. On WDBC and diabetes data, it shows high agreement with SHAP (Spearman rho = 0.9273 and 0.9353), supported by further signed, top-k, magnitude, deletion, and sensitivity analyses. Results position DhondtXAI as a complementary proportional, alliance-aware, and threshold-aware tabular XAI method, not a replacement for SHAP or LIME.

翻译：本研究提出DhondtXAI框架，这是一种独立于SHAP、基于D'Hondt方法的表格数据可解释AI归因框架。DhondtXAI不依赖模型原生特征重要性或SHAP值，而是通过计算背景干预下的移除效应，分离正负证据，形成可选的联盟结构，施加可选阈值，采用D'Hondt规则分配席位，并投影至局部模型输出差异。该方法通过构造保证完备性，同时报告投影残差比率作为诊断指标。我们在合成加性测试与交互测试、相关特征扰动、算子与分配消融、投影模式比较、对数尺度检验、重复分割验证、配对删除测试，以及两个医疗数据集（威斯康星州诊断性乳腺癌数据的CatBoost模型与早期糖尿病风险预测的XGBoost模型）上对该方法进行了评估。SHAP仅作为外部对照方法，采用对齐的参数设置。在加性合成数据中，DhondtXAI精确还原真实排序；在乘性交互作用下，联盟结构将平均投影残差从0.2527降至0.0001。在WDBC和糖尿病数据集上，该方法与SHAP具有高度一致性（Spearman rho分别为0.9273和0.9353），后续的符号分析、top-k分析、幅度分析、删除测试与敏感性分析进一步支持了这一结论。实验结果表明，DhondtXAI是一种互补的比例性、联盟感知、阈值感知的表格XAI方法，而非SHAP或LIME的替代方案。