{"total":39,"rows":[{"id":"2309.09901","title":"The role of causality in explainable artificial intelligence","created":"2023-09-18","cited_by_count":17,"area":null},{"id":"2403.02955","title":"XAI-Based Detection of Adversarial Attacks on Deepfake Detectors","created":"2024-03-05","cited_by_count":9,"area":null},{"id":"2503.21356","title":"Investigating the Duality of Interpretability and Explainability in Machine Learning","created":"2025-03-27","cited_by_count":8,"area":null},{"id":"2406.05090","title":"Provably Better Explanations with Optimized Aggregation of Feature Attributions","created":"2024-06-07","cited_by_count":6,"area":null},{"id":"2307.00309","title":"Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey","created":"2023-07-01","cited_by_count":5,"area":null},{"id":"2401.08513","title":"X Hacking: The Threat of Misguided AutoML","created":"2024-01-16","cited_by_count":5,"area":null},{"id":"2404.18702","title":"Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots","created":"2024-04-29","cited_by_count":5,"area":null},{"id":"2406.06417","title":"Explainable Graph Neural Networks Under Fire","created":"2024-06-10","cited_by_count":5,"area":null},{"id":"2406.14349","title":"When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances","created":"2024-06-20","cited_by_count":5,"area":null},{"id":"2311.17228","title":"Survey on AI Ethics: A Socio-technical Perspective","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2405.03820","title":"False Sense of Security in Explainable Artificial Intelligence (XAI)","created":"2024-05-06","cited_by_count":4,"area":null},{"id":"2407.16233","title":"Algebraic Adversarial Attacks on Integrated Gradients","created":"2024-07-23","cited_by_count":4,"area":null},{"id":"2408.06509","title":"Fooling SHAP with Output Shuffling Attacks","created":"2024-08-12","cited_by_count":4,"area":null},{"id":"2506.15499","title":"Pixel-level Certified Explanations via Randomized Smoothing","created":"2025-06-18","cited_by_count":4,"area":null},{"id":"2503.08636","title":"Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2508.20040","title":"Model Science: getting serious about verification, explanation and control of AI systems","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2511.10439","title":"Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration","created":"2025-11-13","cited_by_count":3,"area":null},{"id":"2406.09069","title":"On the Robustness of Global Feature Effect Explanations","created":"2024-06-13","cited_by_count":2,"area":null},{"id":"2408.01715","title":"Joint Universal Adversarial Perturbations with Interpretations","created":"2024-08-03","cited_by_count":2,"area":null},{"id":"2409.17774","title":"Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations","created":"2024-09-26","cited_by_count":2,"area":null},{"id":"2411.00839","title":"CausAdv: A Causal-based Framework for Detecting Adversarial Examples","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2502.14572","title":"Factor Graph-based Interpretable Neural Networks","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2507.11099","title":"A Survey on Interpretability in Visual Recognition","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2402.02452","title":"XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics","created":"2024-02-04","cited_by_count":1,"area":null},{"id":"2510.03623","title":"Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications","created":"2025-10-04","cited_by_count":1,"area":null},{"id":"2403.09863","title":"Towards White Box Deep Learning","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2403.16569","title":"Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors","created":"2024-03-25","cited_by_count":0,"area":null},{"id":"2406.03012","title":"Towards Understanding the Influence of Training Samples on Explanations","created":"2024-06-05","cited_by_count":0,"area":null},{"id":"2410.18215","title":"Advancing NLP Security by Leveraging LLMs as Adversarial Engines","created":"2024-10-23","cited_by_count":0,"area":null},{"id":"2503.12683","title":"Algebraic Adversarial Attacks on Explainability Models","created":"2025-03-16","cited_by_count":0,"area":null},{"id":"2506.20102","title":"Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox","created":"2025-06-25","cited_by_count":0,"area":null},{"id":"2507.22832","title":"Pulling Back the Curtain on Deep Networks","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2511.20236","title":"Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2601.04977","title":"On the Definition and Detection of Cherry-Picking in Counterfactual Explanations","created":"2026-01-08","cited_by_count":0,"area":null},{"id":"2602.08170","title":"Evasion of IoT Malware Detection via Dummy Code Injection","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2603.06523","title":"SCAN: Visual Explanations with Self-Confidence and Analysis Networks","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2603.29915","title":"Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2604.26219","title":"eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2607.25529","title":"Are the High-weight Neurons the Important Ones in Image Classification Neural Networks?","created":"2026-07-28","cited_by_count":0,"area":null}],"next_cursor":null}