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As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies.
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’It’s Reducing a Human Being to a Percentage’ Perceptions of Justice in Algorithmic Decisions. In CHI . 1–14
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Audio adversarial examples: Targeted attacks on speech-to-text. In SPW . 1–7
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Explainable AI in industry. In KDD . 3203–3204
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Twin-Systems to Explain Artificial Neural Networks using Case-Based Reasoning: Comparative Tests of Feature-Weighting Methods in ANN-CBR Twins for XAI. In IJCAI . 2708–2715
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Inferring Sensitive Attributes from Model Explanations. In CIKM . 416–425
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k-Means SubClustering: A Differentially Private Algorithm with Improved Clustering Quality. In CIKM
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Accurate, Explainable, and Private Models: Providing Recourse While Minimizing Training Data Leakage
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XorSHAP: Privacy-Preserving Explainable AI for Decision Tree Models
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Balancing Privacy Protection and Interpretability in Federated Learning
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Private Graph Extraction via Feature Explanations
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An Overview of the ICASSP Special Session on AI Security and Privacy in Speech and Audio Processing. In ACM Multimedia workshop
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Feature-based learning for diverse and privacy-preserving counterfactual explanations. In KDD . 2211–2222
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Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience. In ECML PKDD
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Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation. In AAAI , Vol. 38. 19902–19910
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