{"total":126,"rows":[{"id":"2202.06861","title":"Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond","created":"2022-02-14","cited_by_count":171,"area":null},{"id":"2310.19775","title":"Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions","created":"2023-10-30","cited_by_count":125,"area":null},{"id":"2402.05602","title":"AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers","created":"2024-02-08","cited_by_count":120,"area":"lr"},{"id":"2101.05307","title":"Explainability of deep vision-based autonomous driving systems: Review and challenges","created":"2021-01-13","cited_by_count":80,"area":null},{"id":"2309.12325","title":"FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare","created":"2023-08-11","cited_by_count":60,"area":null},{"id":"2106.13200","title":"Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy","created":"2021-06-24","cited_by_count":55,"area":null},{"id":"2306.06123","title":"Adversarial attacks and defenses in explainable artificial intelligence: A survey","created":"2023-06-06","cited_by_count":48,"area":null},{"id":"2302.07265","title":"The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus","created":"2023-02-14","cited_by_count":37,"area":null},{"id":"2110.08412","title":"Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining","created":"2021-10-15","cited_by_count":31,"area":null},{"id":"2208.09418","title":"SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability","created":"2022-08-19","cited_by_count":28,"area":null},{"id":"2401.06654","title":"Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks","created":"2024-01-12","cited_by_count":28,"area":null},{"id":"2508.17298","title":"Explain Before You Answer: A Survey on Compositional Visual Reasoning","created":"2025-08-24","cited_by_count":28,"area":"lr"},{"id":"2303.00652","title":"Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science","created":"2023-03-01","cited_by_count":27,"area":null},{"id":"2311.00308","title":"From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities","created":"2023-11-01","cited_by_count":25,"area":null},{"id":"2211.12486","title":"Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations","created":"2022-11-22","cited_by_count":22,"area":null},{"id":"2402.13914","title":"Position: Explain to Question not to Justify","created":"2024-02-21","cited_by_count":20,"area":null},{"id":"2109.05327","title":"An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability","created":"2021-09-11","cited_by_count":16,"area":null},{"id":"2204.02380","title":"CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations","created":"2022-04-05","cited_by_count":16,"area":"lr"},{"id":"2409.16756","title":"Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics","created":"2024-09-25","cited_by_count":16,"area":null},{"id":"2311.01961","title":"Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets","created":"2023-11-03","cited_by_count":15,"area":null},{"id":"2204.11642","title":"Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset","created":"2022-04-25","cited_by_count":13,"area":null},{"id":"2112.10161","title":"RELAX: Representation Learning Explainability","created":"2021-12-19","cited_by_count":12,"area":null},{"id":"2206.11212","title":"VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives","created":"2022-06-22","cited_by_count":12,"area":"lr"},{"id":"2211.17174","title":"Optimizing Explanations by Network Canonization and Hyperparameter Search","created":"2022-11-30","cited_by_count":11,"area":null},{"id":"2502.15886","title":"A Close Look at Decomposition-based XAI-Methods for Transformer Language Models","created":"2025-02-21","cited_by_count":11,"area":null},{"id":"2305.18011","title":"Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition","created":"2023-05-29","cited_by_count":10,"area":null},{"id":"2204.05044","title":"From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology","created":"2022-04-11","cited_by_count":9,"area":null},{"id":"2205.03398","title":"Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning","created":"2022-05-06","cited_by_count":9,"area":null},{"id":"2401.07803","title":"Uncovering the Full Potential of Visual Grounding Methods in VQA","created":"2024-01-15","cited_by_count":9,"area":null},{"id":"2306.12150","title":"Benchmarking the Influence of Pre-training on Explanation Performance in MR Image Classification","created":"2023-06-21","cited_by_count":8,"area":null},{"id":"2411.02470","title":"Benchmarking XAI Explanations with Human-Aligned Evaluations","created":"2024-11-04","cited_by_count":8,"area":"alignment"},{"id":"2306.12816","title":"XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance","created":"2023-06-22","cited_by_count":7,"area":null},{"id":"2406.05068","title":"Classification Metrics for Image Explanations: Towards Building Reliable XAI-Evaluations","created":"2024-06-07","cited_by_count":7,"area":null},{"id":"2501.15374","title":"Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models","created":"2025-01-26","cited_by_count":7,"area":null},{"id":"2502.04695","title":"Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2503.05050","title":"A Unified Framework with Novel Metrics for Evaluating the Effectiveness of XAI Techniques in LLMs","created":"2025-03-06","cited_by_count":7,"area":null},{"id":"2310.08537","title":"Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations","created":"2023-10-12","cited_by_count":6,"area":null},{"id":"2404.11330","title":"Toward Understanding the Disagreement Problem in Neural Network Feature Attribution","created":"2024-04-17","cited_by_count":6,"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":"2411.05841","title":"FLEXtime: Filterbank learning to explain time series","created":"2024-11-06","cited_by_count":6,"area":null},{"id":"2310.07819","title":"Faithfulness Measurable Masked Language Models","created":"2023-10-11","cited_by_count":5,"area":null},{"id":"2310.18046","title":"ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese","created":"2023-10-27","cited_by_count":5,"area":"lr"},{"id":"2406.11547","title":"GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations","created":"2024-06-17","cited_by_count":5,"area":null},{"id":"2406.18253","title":"On the Role of Visual Grounding in VQA","created":"2024-06-26","cited_by_count":5,"area":null},{"id":"2407.16653","title":"Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models","created":"2024-07-23","cited_by_count":5,"area":null},{"id":"2409.13684","title":"The FIX Benchmark: Extracting Features Interpretable to eXperts","created":"2024-09-20","cited_by_count":5,"area":null},{"id":"2409.14590","title":"Explainable AI needs formalization","created":"2024-09-22","cited_by_count":5,"area":null},{"id":"2207.04812","title":"A clinically motivated self-supervised approach for content-based image retrieval of CT liver images","created":"2022-07-11","cited_by_count":4,"area":null},{"id":"2301.13445","title":"A Survey of Explainable AI in Deep Visual Modeling: Methods and Metrics","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2302.05624","title":"A novel approach to generate datasets with XAI ground truth to evaluate image models","created":"2023-02-11","cited_by_count":4,"area":null},{"id":"2307.13345","title":"Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type","created":"2023-07-25","cited_by_count":4,"area":null},{"id":"2312.07252","title":"Identifying Drivers of Predictive Aleatoric Uncertainty","created":"2023-12-12","cited_by_count":4,"area":null},{"id":"2406.13584","title":"FreqRISE: Explaining time series using frequency masking","created":"2024-06-19","cited_by_count":4,"area":null},{"id":"2412.05592","title":"From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation","created":"2024-12-07","cited_by_count":4,"area":null},{"id":"2501.13887","title":"What Does an Audio Deepfake Detector Focus on? A Study in the Time Domain","created":"2025-01-23","cited_by_count":4,"area":null},{"id":"2502.16697","title":"Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations","created":"2025-02-23","cited_by_count":4,"area":null},{"id":"2211.08425","title":"A Rigorous Study Of The Deep Taylor Decomposition","created":"2022-11-14","cited_by_count":3,"area":null},{"id":"2310.06514","title":"AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments","created":"2023-10-10","cited_by_count":3,"area":null},{"id":"2310.14957","title":"XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification","created":"2023-10-23","cited_by_count":3,"area":null},{"id":"2311.04813","title":"Be Careful When Evaluating Explanations Regarding Ground Truth","created":"2023-11-08","cited_by_count":3,"area":null},{"id":"2405.12261","title":"EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods","created":"2024-05-20","cited_by_count":3,"area":null},{"id":"2504.19249","title":"ODExAI: A Comprehensive Object Detection Explainable AI Evaluation","created":"2025-04-27","cited_by_count":3,"area":null},{"id":"2505.01198","title":"Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2507.01722","title":"When Does Pruning Benefit Vision Representations?","created":"2025-07-02","cited_by_count":3,"area":null},{"id":"2507.05916","title":"On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classification","created":"2025-07-08","cited_by_count":3,"area":null},{"id":"2405.02344","title":"A Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attributions","created":"2024-05-02","cited_by_count":2,"area":null},{"id":"2405.02583","title":"Explainable Interface for Human-Autonomy Teaming: A Survey","created":"2024-05-04","cited_by_count":2,"area":null},{"id":"2405.17523","title":"Locally Testing Model Detections for Semantic Global Concepts","created":"2024-05-27","cited_by_count":2,"area":null},{"id":"2408.11401","title":"Revisiting FunnyBirds evaluation framework for prototypical parts networks","created":"2024-08-21","cited_by_count":2,"area":null},{"id":"2410.07711","title":"AdaptGrad: Adaptive Sampling to Reduce Noise","created":"2024-10-10","cited_by_count":2,"area":null},{"id":"2412.13333","title":"Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language Model's Prediction Rationality","created":"2024-12-17","cited_by_count":2,"area":"mllm"},{"id":"2507.11099","title":"A Survey on Interpretability in Visual Recognition","created":"2025-07-15","cited_by_count":2,"area":null},{"id":"2602.07047","title":"ShapBPT: Image Feature Attributions Using Data-Aware Binary Partition Trees","created":"2026-02-04","cited_by_count":2,"area":null},{"id":"2302.10764","title":"On The Coherence of Quantitative Evaluation of Visual Explanations","created":"2023-02-14","cited_by_count":1,"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":"2403.17647","title":"Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering","created":"2024-03-26","cited_by_count":1,"area":null},{"id":"2404.10387","title":"CNN-based explanation ensembling for dataset, representation and explanations evaluation","created":"2024-04-16","cited_by_count":1,"area":null},{"id":"2404.14271","title":"Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation","created":"2024-04-22","cited_by_count":1,"area":null},{"id":"2405.10552","title":"Data Science Principles for Interpretable and Explainable AI","created":"2024-05-17","cited_by_count":1,"area":null},{"id":"2407.06059","title":"LaFAM: Unsupervised Feature Attribution with Label-free Activation Maps","created":"2024-07-08","cited_by_count":1,"area":null},{"id":"2503.19813","title":"Guidelines For The Choice Of The Baseline in XAI Attribution Methods","created":"2025-03-25","cited_by_count":1,"area":null},{"id":"2504.10527","title":"Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review","created":"2025-04-12","cited_by_count":1,"area":null},{"id":"2505.10515","title":"PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models","created":"2025-05-15","cited_by_count":1,"area":null},{"id":"2505.11210","title":"Minimizing False-Positive Attributions in Explanations of Non-Linear Models","created":"2025-05-16","cited_by_count":1,"area":null},{"id":"2506.01059","title":"XAI-Units: Benchmarking Explainability Methods with Unit Tests","created":"2025-06-01","cited_by_count":1,"area":null},{"id":"2507.07148","title":"Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey","created":"2025-07-09","cited_by_count":1,"area":null},{"id":"2602.08715","title":"Exploring SAIG Methods for an Objective Evaluation of XAI","created":"2026-02-09","cited_by_count":1,"area":null},{"id":"2602.09278","title":"The effect of whitening on explanation performance","created":"2026-02-09","cited_by_count":1,"area":null},{"id":"2603.06781","title":"xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2605.12816","title":"AGOP as Explanation: From Feature Learning to Per-Sample Attribution in Image Classifiers","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2606.31742","title":"FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2305.05349","title":"Towards the Characterization of Representations Learned via Capsule-based Network Architectures","created":"2023-05-09","cited_by_count":0,"area":null},{"id":"2311.01894","title":"Simulation of acquisition shifts in T2 Flair MR images to stress test AI segmentation networks","created":"2023-11-03","cited_by_count":0,"area":null},{"id":"2401.03093","title":"XXAI: Towards eXplicitly eXplainable Artificial Intelligence","created":"2024-01-05","cited_by_count":0,"area":null},{"id":"2401.17200","title":"NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques","created":"2024-01-30","cited_by_count":0,"area":null},{"id":"2403.19820","title":"Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach","created":"2024-03-28","cited_by_count":0,"area":null},{"id":"2404.16884","title":"Aligning Knowledge Graphs Provided by Humans and Generated from Neural Networks in Specific Tasks","created":"2024-04-23","cited_by_count":0,"area":null},{"id":"2405.12175","title":"Enhancing Explainable AI: A Hybrid Approach Combining GradCAM and LRP for CNN Interpretability","created":"2024-05-20","cited_by_count":0,"area":null},{"id":"2406.12150","title":"CHAOSMINING: Benchmarking Post-Hoc Attribution with Sparse Informative Features in High Dimensions","created":"2024-06-17","cited_by_count":0,"area":null},{"id":"2407.00371","title":"Axiomatization of Gradient Smoothing in Neural Networks","created":"2024-06-29","cited_by_count":0,"area":null}],"next_cursor":"100"}