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On the Robustness of Interpretability Methods
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We argue that robustness of explanations---i.e., that similar inputs should give rise to similar explanations---is a key desideratum for interpretability. We introduce metrics to quantify robustness and demonstrate that current methods do not perform well according to these metrics. Finally, we propose ways that robustness can be enforced on existing interpretability approaches.
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Cited by 20 Pith papers
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Architecture-Aware Explanation Auditing for Industrial Visual Inspection
An audit protocol on wafer maps finds that ViT-Tiny with Attention Rollout achieves better deletion faithfulness than other models and explainers, with readout structure as the key factor and RISE outperforming native...
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Architecture-Aware Explanation Auditing for Industrial Visual Inspection
Explanation faithfulness for deep classifiers on wafer maps is highest when the explainer matches the model's native readout structure, with ViT-Tiny plus Attention Rollout achieving lower Deletion AUC than mismatched...
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Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions
Outcome-fair credit models often exhibit hidden procedural bias through inconsistent reasoning across groups, which the CEC framework mitigates by enforcing consistent feature attributions via counterfactuals.
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Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?
FASS benchmark shows post-hoc attributions remain unstable under geometric perturbations even after filtering for unchanged predictions, with Grad-CAM exhibiting the highest stability across ImageNet, COCO, and CIFAR-10.
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In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks
In-context symbolic regression methods improve robustness of symbolic formula recovery from KANs, cutting median OFAT test MSE by up to 99.8 percent across hyperparameter sweeps.
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Extremal Contours: Gradient-driven contours for compact visual attribution
A training-free method using Fourier-parameterized star-convex contours optimized via gradients to generate compact, faithful visual attributions for image classifiers on benchmarks like ImageNet.
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Architecture-Aware Explanation Auditing for Industrial Visual Inspection
The paper proposes an architecture-aware explanation audit protocol demonstrating that perturbation-based faithfulness is bounded by structural compatibility between explainer and model readout rather than architectur...
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Interpretability Can Be Actionable
Interpretability research should be judged by actionability—the degree to which its insights support concrete decisions and interventions—rather than explanatory power alone.
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Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE
Static malware classifiers learn packing artifacts and dataset composition biases rather than malicious semantics, as diagnosed by TRUSTEE interpretability across controlled dataset variations.
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generali...
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NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
NEURON raises AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV heart-failure mortality prediction while lifting human-aligned explanation scores from 0.50 to 0.85 by grounding SHAP values in SNOMED CT and patient notes via...
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A Unified Framework for Evaluating and Enhancing the Transparency of Explainable AI Methods via Perturbation-Gradient Consensus Attribution
Introduces a unified evaluation framework for XAI using five principled metrics and the PGCA method that fuses grid perturbation with Grad-CAM++ , reporting top scores in fidelity, interpretability and fairness on Res...
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Explaining Predictions from Tree-based Boosting Ensembles
Develops a method to find minimal input perturbations that flip GBDT predictions by extending random-forest counterfactuals to account for sequential tree dependencies and negative-gradient training.
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GESD: Beyond Outcome-Oriented Fairness
The paper proposes GESD, a procedural fairness metric for group disparities in explanation stability and robustness, and integrates it into the FEU multi-objective optimization framework.
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Multi-Dimensional Model Integrity and Responsibility Assessment Index and Scoring Framework
MIRAI is a unified index that combines five responsibility dimensions into one score for tabular models, demonstrating that predictive performance does not ensure high overall integrity.
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NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
NEURON integrates SNOMED CT, ML, and RAG LLM to raise AUC from 0.74-0.77 to 0.84-0.88 and human-aligned explainability scores from 0.50 to 0.85 on MIMIC-IV acute heart failure data.
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Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs
HETA is a new attribution framework for decoder-only LLMs that combines semantic transition vectors, Hessian-based sensitivity scores, and KL divergence to produce more faithful and human-aligned token attributions th...
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ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
ProtoSiTex introduces dual-phase prototype learning with hierarchical consistency loss for semi-interpretable multi-label text classification on a new subsentence-annotated hotel review dataset.
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Does Role Specialization Matter for Explanation Faithfulness in Mixture-of-Experts?
Representation decorrelation regularization in MoE models improves explanation faithfulness on multimodal benchmarks while preserving task performance.
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Explainable AI for Next-Generation Wireless Physical Layer: Basics, State-of-the-Art, and Open Challenges
A survey formalizing responsibility-oriented goals for wireless XAI, developing a taxonomy of explainability approaches, reviewing PHY layer applications, and discussing open challenges including performance tradeoffs...
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