ScholarWorks@경상국립대학교

초록

Argumentative explainable artificial intelligence employs argumentation theory to explain the mechanisms of machine learning. Previous approaches for explaining deep learning models collectively compressed layers via clustering. However, this resulted in accumulated information loss across layers, thereby degrading the fidelity of explanations. We propose online activation value-aware clustering and aggregation, a compression algorithm that preserves the inference structure of the original neural network with greater fidelity. The proposed method sequentially compresses each layer, immediately recalculates activation values following compression, and rectifies inter-layer information loss using a singular-value-scaled ridge alignment approach. To evaluate the effectiveness of the proposed method, we introduce four novel quantitative metrics. Input-output fidelity and structural fidelity measure how accurately the compressed model preserves the original model predictions and internal activations. Input-output perturbation consistency and structural perturbation consistency assess the similarity of the changes induced by Gaussian-perturbed input data. Experiments on three benchmark datasets (Breast Cancer, California Housing, and HIGGS) demonstrate that our method achieves performance improvements ranging from 12.9% to 53.7% across the four metrics, demonstrating significantly higher explanation fidelity than existing approaches.

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