Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

UCI Machine Learning Repository

Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839

The Mythos of Model Interpretability: In machine learning, the concept of interpretability is both important and slippery.

Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524

Fairness through awareness

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501

Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

Papernot, N., McDaniel, P. D., Wu, X., Jha, S., & Swami, A. (2016) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2016.41 · Cited by 3,251

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121

Axiomatic Characterization of Data-Driven Influence Measures for Classification

Sliwinski, J., Strobel, M., & Zick, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301718 · Cited by 5

Learning Representations by Humans, for HumansPreprint

Hilgard, S., Rosenfeld, N., Banaji, M. R., Cao, J., & Parkes, D. C. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/4nvts · Cited by 6

SVAP: Shapley Value Guided Attribution Prior for Neural Network-Based Autonomous Driving

Li, M., Sun, H., Huang, Y., & Chen, H. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3575760 · Cited by 4

Classifying polish in use-wear analysis with convolutional neural networks

Eleftheriadou, A., Djellal, Y., McPherron, S. P., & Marreiros, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-18179-4 · Cited by 4

Assessing Out-of-Domain Language Model Performance from Few Examples

Singhal, P., Forristal, J., Ye, X., & Durrett, G. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.175 · Cited by 5

Revision Transformers: Instructing Language Models to Change Their Values

Friedrich, F., Stammer, W., Schramowski, P., & Kersting, K. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230341 · Cited by 6

In-Training Explainability Frameworks: A Method to Make Black-Box Machine Learning Models More Explainable

Acun, C., & Nasraoui, O. (2023) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat59888.2023.00036 · Cited by 4

Identify and mitigate bias in electronic phenotyping: A comprehensive study from computational perspective

Ding, S., Zhang, S., Hu, X., & Zou, N. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104671 · Cited by 7

Near Hit and Near Miss Example Explanations for Model Revision in Binary Image Classification

Finzel, B., Knoblach, J., Thaler, A. M., & Schmid, U. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77738-7_22 · Cited by 4

Beyond the Visual Analysis of Deep Model Saliency

Bargal, S. A., Zunino, A., Petsiuk, V., Zhang, J., Murino, V., Sclaroff, S., & Saenko, K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_13 · Cited by 4

Learn decision trees with deep visual primitives

Xue, M., Zhang, H., Huang, Q., Song, J., & Song, M. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103682 · Cited by 5

Providing Post-Hoc Explanation for Node Representation Learning Models Through Inductive Conformal Predictions

Park, H. (2022) · IEEE Access · doi:10.1109/access.2022.3233036 · Cited by 5

Calibrating a Transformer-Based Model’s Confidence on Community-Engaged Research Studies: Decision Support Evaluation Study

Ferrell, B., Raskin, S. E., & Zimmerman, E. B. (2023) · JMIR Formative Research · doi:10.2196/41516 · Cited by 5

Similarizing the Influence of Words with Contrastive Learning to Defend Word-level Adversarial Text Attack

Zhan, P., Jing, Y., Wang, H., Zheng, C., Huang, X. B., & Wang, L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.500 · Cited by 4

Using Interpretation Methods for Model Enhancement

Chen, Z., Jiang, C., & Tu, K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.28 · Cited by 4

Distance-Aware eXplanation Based Learning

Hagos, M. T., Belton, N., Curran, K. M., & Namee, B. M. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00048 · Cited by 5

Beyond Debiasing: Actively Steering Feature Selection via Loss Regularization

Blunk, J., Penzel, N., Bodesheim, P., & Denzler, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-54605-1_26 · Cited by 4

Shapley Values and Meta-Explanations for Probabilistic Graphical Model Inference

Liu, Y., Chen, C., Liu, Y., Zhang, X., & Xie, S. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411881 · Cited by 4

Practical Benefits of Feature Feedback Under Distribution Shift

Katakkar, A., Yoo, C. H., Wang, W., Lipton, Z. C., & Kaushik, D. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.29 · Cited by 5

Evaluating Gender-Neutral Training Data for Automated Image Captioning

Amend, J. J., Wazzan, A., & Souvenir, R. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671774 · Cited by 4

Explanatory Dialogs

Bansal, G. (2018) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3278721.3278795 · Cited by 4

Making It Simple? Training Deep Learning Models Toward Simplicity

Repetto, M., & Torre, D. L. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9765248 · Cited by 4

Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix FactorizationPreprint

Kim, J., & Seo, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06201 · Cited by 4

Fine-grained Uncertainty Modeling in Neural NetworksPreprint

Soni, R. K., Shah, N., & Moore, J. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04205 · Cited by 4

Using Depth for Improving Referring Expression Comprehension in Real-World EnvironmentsPreprint

Doğan, F. I., & Leite, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04658 · Cited by 5

Rethinking the judicial duty to state reasons in the age of automation?

Hendrickx, V. (2025) · Cambridge Forum on AI Law and Governance · doi:10.1017/cfl.2025.11 · Cited by 8

Evaluating Model Interpretability in Speech-Based Clinical Artificial Intelligence Systems

Xu, L., Berisha, V., Utianski, R. L., & Liss, J. (2025) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/2025_persp-25-00003 · Cited by 4

Explanation-Guided Adversarial Training for Robust and Interpretable Models

Chen, C., Chen, Y., Lin, S., Hong, D., Wu, S., Liao, X., & Liu, C. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3666191 · Cited by 3

Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews

Lee, Y., Kim, J.-H., Kim, D., Kho, Y., Kim, Y., & Kang, P. (2023) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/2023.wassa-1.20 · Cited by 4

Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation

Bender, S., Anders, C. J., Chormai, P., Marxfeld, H., Herrmann, J., & Montavon, G. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00275 · Cited by 3

Global explanation supervision for Graph Neural Networks

Etemadyrad, N., Gao, Y., Dinakarrao, S. M. P., & Zhao, L. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1410424 · Cited by 3

Right on Time: Revising Time Series Models by Constraining Their Explanations

Kraus, M., Steinmann, D. B., Wüst, A., Kokozinski, A., & Kersting, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06109-6_28 · Cited by 4

Investigating the Compositional Structure of Deep Neural Networks

Craighero, F., Angaroni, F., Graudenzi, A., Stella, F. A., & Antoniotti, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-64583-0_30 · Cited by 3

Review of measures for improving ML model interpretability: Empowering decision makers with transparent insights

Arsić, S., Mihic, M. M., Petrović, D. Č., Mitrović, Z., Kostić, S. C., & Mihić, O. R. (2024) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa59508.2024.10467907 · Cited by 3

Improving ViT interpretability with patch-level mask prediction

Kang, J., Heo, B., & Choe, J. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.11.018 · Cited by 3

IDEAL: A malicious traffic detection framework with explanation-guided learning

Jia, H., Lang, B., Li, X., & Yan, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113419 · Cited by 4

Transferring Information Between Neural Networks

Ehmann, C., & Samek, W. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461511 · Cited by 3

XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models

Lee, D., Kadakia, A., Joshi, B., Chan, A., Liu, Z., Narahari, K., Shibuya, T., Mitani, R., Sekiya, T., Pujara, J., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.25 · Cited by 4

Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields

Zhang, Y., & Ramesh, A. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/838 · Cited by 3

Explainable Deep Modeling of Tabular Data using TableGraphNetPreprint

Terejanu, G., Chowdhury, J., Rashid, R., & Chowdhury, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.05205 · Cited by 4

Toward a Unified Framework for Debugging Gray-box Models.Preprint

Bontempelli, A., Giunchiglia, F., Passerini, A., & Teso, S. (2021) · Iris (University of Trento) · doi:10.48550/arxiv.2109.11160 · Cited by 3

Proceedings of the First Workshop on Trustworthy Natural Language Processing

(2021) · doi:10.18653/v1/2021.trustnlp-1 · Cited by 3

Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability

Zafar, M. R., & Khan, N. (2023) · doi:10.32920/22734320.v1 · Cited by 3

Ensembles of Locally Independent Prediction Models

Ross, A. S., Pan, W., Celi, L. A., & Doshi‐Velez, F. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6004 · Cited by 5

Faster ISNet for Background Bias Mitigation on Deep Neural Networks

Bassi, P. R. A. S., Decherchi, S., & Cavalli, A. (2024) · IEEE Access · doi:10.1109/access.2024.3461773 · Cited by 2

Regulating Model Reliance on Non-robust Features by Smoothing Input Marginal Density

Yang, P., Akhtar, N., Shah, M. A., & Mian, A. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_19 · Cited by 4

Space-scale exploration of the poor reliability of deep learning models: the case of the remote sensing of rooftop photovoltaic systems

Kasmi, G., Dubus, L., Saint‐Drenan, Y., & Blanc, P. (2025) · Environmental Data Science · doi:10.1017/eds.2025.13 · Cited by 5

Toward trustworthy AI with integrative explainable AI frameworks

Finzel, B. (2025) · it - Information Technology · doi:10.1515/itit-2025-0007 · Cited by 4

Explainable AI-Powered Autonomous Systems: Enhancing Trust and Transparency in Critical Applications

Aruna, S., Srilakshmi, K., Praveena, K., Veena, T., Prakash, B. P., & Jayapoorani, S. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.2494 · Cited by 2

Joint explainable and fair AI in healthcare

Shahbazian, R., Trubitsyna, I., & Mirtaheri, S. L. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03233-y · Cited by 2

Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception

Sielemann, A., Barner, V., Wolf, S., Roschani, M., Ziehn, J. R., & Beyerer, J. (2025) · IEEE International Automated Vehicle Validation Conference (IAVVC) · doi:10.1109/iavvc61942.2025.11219547 · Cited by 2

Explainable Artificial Intelligence for Deepfake Detection: Pipeline, Open Source and Comparisons

Li, H., Chen, J., Wang, B., García, S., & Camacho, D. (2026) · Expert Systems · doi:10.1111/exsy.70222 · Cited by 4

AIM: Amending Inherent Interpretability via Self-Supervised Masking

Alshami, E., Agnihotri, S., Schiele, B., & Keuper, M. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00100 · Cited by 2

How Does DCNN Make Decisions ?

Lin, Y., Wang, N., Ma, X., Li, Z., & Bai, G. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9411984 · Cited by 2

Human-in-the-loop deep learning retinal image classification with customized loss function

Shakya, S., Vasquez, M., Wang, Y., Tchoua, R., Furst, J. D., & Raicu, D. S. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2611173 · Cited by 2

Erklärbare KI in der medizinischen Diagnose – Erfolge und Herausforderungen

Lucieri, A., Bajwa, M. N., Dengel, A. R., & Ahmed, S. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_35 · Cited by 2

Fault Report Generation for ICT Systems by Jointly Learning Time-series and Text Data

Fukuda, N., Wu, C., Horiuchi, S., & Tayama, K. (2022) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms54207.2022.9789784 · Cited by 3

A Systematic Approach for Tracking the Evolution of XAI as a Field of Research

Jamshidi, S., Nowaczyk, S., Fanaee‐T, H., & Rahat, M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23633-4_31 · Cited by 2

Learning to Ignore Adversarial Attacks

Zhang, Y., Zhou, Y., Carton, S., & Tan, C. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.216 · Cited by 2

Towards Background and Foreground Color Robustness with Adversarial Right for the Right Reasons

Santos, F., de Souza, M. D., & Zanchettin, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44192-9_14 · Cited by 2

Evolving interpretable neural modularity in free-form multilayer perceptrons through connection costs

van der Merwe, A. W., & Vandenheever, D. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09117-4 · Cited by 2

REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization

Madani, M. R. G., & Minervini, P. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.40 · Cited by 5

Exploring Multi-Task Learning for Explainability

Charalampakos, F., & Koutsopoulos, I. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50396-2_20 · Cited by 2

Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression

Bareeva, D., Dreyer, M., Pahde, F., Samek, W., & Lapuschkin, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00357 · Cited by 3

AI-Based Security Solutions for Protecting Communication Networks

Goel, P. K., & Gupta, V. (2024) · Advances in wireless technologies and telecommunication book series · doi:10.4018/979-8-3693-6552-6.ch004 · Cited by 2

Optimizing Local Explainability in Robotic Grasp Failure Prediction

Acun, C., Ashary, A., Popa, D. O., & Nasraoui, O. (2025) · Electronics · doi:10.3390/electronics14122363 · Cited by 2

Interpretability-Aware Vision Transformer

Qiang, Y., Li, C., Zhu, H., Khanduri, P., & Zhu, D. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227842 · Cited by 2

Fine-Grained Explanations Using Markov Logic

Farabi, K. M. A., Sarkhel, S., Dey, S., & Venugopal, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46147-8_37 · Cited by 2

Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training

Zhan, P., Wu, Y., Zhou, S., Zhang, Y., & Wang, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.175 · Cited by 2

Interpreting and Improving Deep-Learning Models with Reality Checks

Singh, C., Ha, W., & Yu, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_12 · Cited by 2

Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data

Sullivan, J., Brackenbury, W., McNutt, A. M., Bryson, K., Byll, K., Chen, Y., Littman, M. L., Tan, C., & Ur, B. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.38 · Cited by 2

Multi-Grained Interpre table Network for Image Recognition

Yang, P., Wen, Z., & Mian, A. S. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956087 · Cited by 2

Prior-Guided Attribution of Deep Neural Networks for Obstetrics and Gynecology

Bonnard, J., Dapogny, A., Zsámboki, R., Braud, L. V. D., Jurkovic, D., Bailly, K., & Dhombres, F. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3337511 · Cited by 2

Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts

Han, D., Choe, J., Chun, S.-Y., Chung, J. J. Y., Chang, M., Yun, S., Song, J. Y., & Oh, S. J. (2023) · IEEE International Conference on Computer Vision · doi:10.1109/iccv51070.2023.01848 · Cited by 5

Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients

Nauta, M., van Putten, M. J. A. M., Tjepkema‐Cloostermans, M. C., Bos, J. P., van Keulen, M., & Seifert, C. (2020) · University of Twente Research Information · Cited by 2

Group Decision Making as Consistency Measure for Learning To Rank

Fenza, G., Gallo, M., Loia, V., Nota, F. D., Orciuoli, F., & Herrera‐Viedma, E. (2021) · IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) · doi:10.1109/civemsa52099.2021.9493582 · Cited by 2

On the explainability of hospitalization prediction on a large COVID-19 patient dataset

Girardi, I., Vagenas, P., Arcos-Díaz, D., Bessaï, L., Büsser, A., Furlan, L., Furlan, R., Gatti, M., Giovannini, A. A., Hoeven, E., et al. (2021) · PubMed Central · doi:10.48550/arxiv.2110.15002 · Cited by 2

Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)Preprint

Plumb, G., Al-Shedivat, M., Xing, E. P., & Talwalkar, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01431 · Cited by 3

Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box ModelsPreprint

Rafique, H., Wang, T., & Lin, Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.10467 · Cited by 2

Adversarial TCAV -- Robust and Effective Interpretation of Intermediate Layers in Neural NetworksPreprint

Soni, R. K., Shah, N., Seng, C. T., & Moore, J. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03549 · Cited by 2

Clinical Predictive Models for COVID-19: Systematic StudyPreprint

Schwab, P., Schütte, A. D., Dietz, B., & Bauer, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08302 · Cited by 2

Demystifying Deep Neural Networks Through Interpretation: A SurveyPreprint

Dao, G., & Lee, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.07119 · Cited by 3

Partially Interpretable Estimators (PIE): Black-Box-Refined Interpretable Machine LearningPreprint

Wang, T., Yang, J., Li, Y., & Wang, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.02410 · Cited by 2

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in HealthcarePreprint

Shams, Z., Dimanov, B., Kola, S., Simidjievski, N., Andrés-Terré, H., Scherer, P. C., Matjašec, U., Abraham, J., Jamnik, M., & Lió, P. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.22.427799 · Cited by 2

IMPACTX: improving model performance by appropriately constraining the training with teacher explanations

Apicella, A., Giugliano, S., Isgrò, F., Pollastro, A., & Prevete, R. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11564-z · Cited by 2

Localization-Guided Supervision for Robust Medical Image Classification by Vision Transformers

Itzhak, S. B., Kiryati, N., Portnoy, O., & Mayer, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92648-8_8 · Cited by 1

Bridging Accuracy and Explainability: A SHAP-Enhanced CNN for Skin Cancer Diagnosis

Roy, S., Fan, X., Alam, N., Chen, X., Qureshi, R., Wu, J., & Yap, M. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98691-8_6 · Cited by 1

TEMPO: Timestep Explanations for Modeling Preferences in Online Preference-Based RL

Karlaus, J., & Schwenker, F. (2025) · IEEE Access · doi:10.1109/access.2025.3588936 · Cited by 1

Data-driven rainfall-runoff modeling in an urban catchment using microwave link attenuation data

Song, Y., Fencl, M., Bareš, V., & Yang, T. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.134328 · Cited by 1

Large Language Models as Attribution Regularizers for Efficient Model Training

Vukadin, D., Šilić, M., & Delač, G. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08327-2_4 · Cited by 1

Fairness in Machine Learning: A Review for Statisticians

He, X., & Li, Y. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2579579 · Cited by 2

Integrating Explainable AI with Human-in-the-Loop Systems for Transparent Decision-Making in Autonomous Robots

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Ranking-Aware Multiple Instance Learning for Histopathology Slide Classification: Development and Validation Study

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