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

Saliency Learning: Teaching the Model Where to Pay AttentionPreprint

Ghaeini, R., Fern, X. Z., Shahbazi, H., & Tadepalli, P. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.08649 · Cited by 23

Finding and Fixing Spurious Patterns with ExplanationsPreprint

Plumb, G., Ribeiro, M. T., & Talwalkar, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02112 · Cited by 14

Transformer-Empowered Invariant Grounding for Video Question Answering

Li, Y., Wang, X., Xiao, J., Ji, W., & Chua, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3303451 · Cited by 17

Are Data-Driven Explanations Robust Against Out-of-Distribution Data?

Tang, L., Qiao, F., Ma, M., & Peng, X. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00372 · Cited by 15

Natural Language Processing and Text Mining (Turning Unstructured Data into Structured)

Bagheri, A., Giachanou, A., Mosteiro, P. J., & Verberne, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-36678-9_5 · Cited by 13

Learning differentiable logic programs for abstract visual reasoning

Shindo, H., Pfanschilling, V., Dhami, D. S., & Kersting, K. (2024) · Machine Learning · doi:10.1007/s10994-024-06610-2 · Cited by 16

Inherently interpretable machine learning for credit scoring: Optimal classification tree with hyperplane splits

Tu, J., & Wu, Z. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.10.046 · Cited by 18

HLGM: A Novel Methodology for Improving Model Accuracy Using Saliency-Guided High and Low Gradient Masking

Karkehabadi, A., Latibari, B. S., Homayoun, H., & Sasan, A. (2024) · International Conference on Information Science and Technology · doi:10.1109/icist63249.2024.10805345 · Cited by 13

Informed Machine Learning: Excess risk and generalization

Oneto, L., Ridella, S., & Anguita, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130521 · Cited by 21

The Importance of Being Interpretable: Toward an Understandable Machine Learning Encoder for Galaxy Cluster Cosmology

Ntampaka, M., & Vikhlinin, A. A. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac423e · Cited by 13

Studying How to Efficiently and Effectively Guide Models with Explanations

Rao, S., Böhle, M., Parchami-Araghi, A., & Schiele, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00184 · Cited by 13

Evaluating Explainability Methods Intended for Multiple Stakeholders

Martin, K., Liret, A., Wiratunga, N., Owusu, G., & Kern, M. (2021) · Künstliche Intell. · doi:10.1007/s13218-020-00702-6 · Cited by 13

Measuring and Improving Faithfulness of Attention in Neural Machine Translation

Moradi, P., Kambhatla, N., & Sarkar, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.243 · Cited by 14

Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning

Zhang, W., Dimiccoli, M., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517522 · Cited by 21

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.Preprint

Schramowski, P., Stammer, W., Teso, S., Brugger, A., Shao, X., Luigs, H.-G., Mahlein, A., & Kristian Kersting, B. (2020) · arXiv (Cornell University) · Cited by 13

Learning Deep Attribution Priors Based On Prior KnowledgePreprint

Weinberger, E., Janizek, J. D., & Lee, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10065 · Cited by 17

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

Shams, Z., Dimanov, B., Kola, S., Simidjievski, N., Terre, H. A., Scherer, P., Matjašec, U., Abraham, J., Jamnik, M., & Lió, P. (2021) · medRxiv · doi:10.1101/2021.01.25.21250459 · Cited by 15

Next2You: Robust Copresence Detection Based on Channel State Information

Fomichev, M., Abanto-Leon, L. F., Stiegler, M., Molina, A., Link, J., & Hollick, M. (2022) · ACM Transactions on Internet of Things · doi:10.1145/3491244 · Cited by 11

Revisiting Gradient Regularization: Inject Robust Saliency-Aware Weight Bias for Adversarial Defense

Li, Q., Hu, Q., Lin, C., Wu, D., & Shen, C. Q. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3289000 · Cited by 13

Understanding Deep Neural Networks through Input Uncertainties

Thiagarajan, J. J., Kim, I., Anirudh, R., & Bremer, P. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682930 · Cited by 12

Explainable AI for Medical Imaging: Knowledge Matters

Bourdon, P., Ahmed, O. B., Urruty, T., Djemal, K., & Fernández-Maloigne, C. (2012) · Springer eBooks · doi:10.1007/978-3-030-74478-6_11 · Cited by 14

Rationale Aware Contrastive Learning Based Approach to Classify and Summarize Crisis-Related Microblogs

Nguyen, T. H., & Rudra, K. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557426 · Cited by 12

Strategies to Exploit XAI to Improve Classification Systems

Apicella, A., Lorenzo, L. D., Isgrò, F., Pollastro, A., & Prevete, R. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44064-9_9 · Cited by 15

Relation Extraction with Explanation

Shahbazi, H., Fern, X. Z., Ghaeini, R., & Tadepalli, P. V. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.579 · Cited by 14

Explaining Text Matching on Neural Natural Language Inference

Kim, Y., Jang, M., & Allan, J. (2020) · ACM Transactions on Information Systems · doi:10.1145/3418052 · Cited by 12

Learning Words by Drawing Images

Surís, D., Recasens, A., Bau, D., Harwath, D. F., Glass, J. R., & Torralba, A. B. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00213 · Cited by 11

Explaining Local, Global, And Higher-Order Interactions In Deep Learning

Lerman, S., Venuto, C., Kautz, H., & Xu, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00126 · Cited by 11

Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box ModelPreprint

Wang, T., & Lin, Q. (2019) · arXiv.org · doi:10.48550/arxiv.1905.04241 · Cited by 19

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-SeriesPreprint

Oh, J., Wang, J., Tang, S., Sjoding, M. W., & Wiens, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02898 · Cited by 11

Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis

Lim, B. Y., Cahaly, J. P., Sng, C. Y. F., & Chew, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714058 · Cited by 12

What to Learn, and How: Toward Effective Learning from Rationales

Carton, S., Kanoria, S., & Tan, C. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.86 · Cited by 20

Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Li, D., Hu, B., Chen, Q., Xu, T., Tao, J., & Zhang, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21342 · Cited by 13

Minimum relevant features to obtain AI explainable system for predicting breast cancer in WDBC

Agarwal, R., & Madamala, R. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns9.12538 · Cited by 10

Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation

Burkhardt, S., Brugger, J., Wagner, N., Ahmadi, Z., Kersting, K., & Krämer, S. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.642263 · Cited by 11

Ensembling Visual Explanations

Rajani, N. F., & Mooney, R. J. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4_7 · Cited by 10

Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision ModelsPreprint

Wang, D., Yang, Y., Tao, C., Gan, Z., Chen, L., Kong, F., Henao, R., & Carin, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03369 · Cited by 14

Modeling Techniques for Machine Learning Fairness: A SurveyPreprint

Wan, M., Zha, D., Liu, N., & Zou, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03015 · Cited by 10

Perspectives on incorporating expert feedback into model updates

Chen, V., Bhatt, U., Heidari, H., Weller, A., & Talwalkar, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100780 · Cited by 15

SQT: Debiased Visual Question Answering via Shuffling Question Types

Huai, T., Yang, S., Zhang, J., Wang, G., Yu, X., Ma, T., & He, L. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00109 · Cited by 9

Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing

Roy, S., Chergui, H., & Verikoukis, C. V. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3400524 · Cited by 11

Thermostat: A Large Collection of NLP Model Explanations and Analysis Tools

Feldhus, N., Schwarzenberg, R., & Möller, S. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.11 · Cited by 9

An Initial Study of Machine Learning Underspecification Using Feature Attribution Explainable AI Algorithms: A COVID-19 Virus Transmission Case Study

Hinns, J., Fan, X., Liu, S., Kovvuri, V. R. R., Yalcin, M. O., & Roggenbach, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89188-6_24 · Cited by 9

From Hope to Safety: Unlearning Biases of Deep Models via Gradient Penalization in Latent Space

Dreyer, M., Pahde, F., Anders, C. J., Samek, W., & Lapuschkin, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30096 · Cited by 14

Saliency Learning: Teaching the Model Where to Pay Attention

Ghaeini, R., Fern, X. Z., Shahbazi, H., & Tadepalli, P. V. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1404 · Cited by 12

A Rate-Distortion Framework for Explaining Black-Box Model Decisions

Kolek, S., Nguyễn, Đ. A., Levie, R., Bruna, J., & Gitta Kutyniok, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_6 · Cited by 13

Deep Learning and Computational Chemistry

James, T., & Hristozov, D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_5 · Cited by 9

Privileged Attribution Constrained Deep Networks for Facial Expression Recognition

Bonnard, J., Dapogny, A., Dhombres, F., & Bailly, K. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956496 · Cited by 9

Regional Tree Regularization for Interpretability in Black Box ModelsPreprint

Wu, M., Parbhoo, S., Hughes, M. C., Kindle, R. D., Celi, L. A., Zazzi, M., Röth, V., & Doshi‐Velez, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04494 · Cited by 10

Individual Explanations in Machine Learning Models: A Survey for PractitionersPreprint

Carrillo, A., Cantú, L. F., & Noriega-Campero, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.04144 · Cited by 18

Role of locality, fidelity and symmetry regularization in learning explainable representations

Ronco, M., & Camps‐Valls, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126884 · Cited by 10

Explainable monotonic networks and constrained learning for interpretable classification and weakly supervised anomaly detection

Wargnier-Dauchelle, V., Grenier, T., Durand‐Dubief, F., Cotton, F., & Sdika, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.111186 · Cited by 10

Impact of Feedback Type on Explanatory Interactive Learning

Hagos, M. T., Curran, K. M., & Namee, B. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16564-1_13 · Cited by 9

FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis

He, Y., Lou, J., Qin, Z., & Ren, K. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3616599 · Cited by 10

Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction

Lee, S., Niu, R., Zhu, L., Kayser, A. S., & Hsu, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412881121 · Cited by 9

MAGI: Multi-Annotated Explanation-Guided Learning

Zhang, Y., Gu, S., Gao, Y., Pan, B., Yang, X., & Zhao, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00189 · Cited by 9

Detecting and interpreting myocardial infarctions using fully convolutional neural networks.Preprint

Strodthoff, N., & Strodthoff, C. (2018) · arXiv (Cornell University) · Cited by 8

Gaussian Process-Based Models for Clinical Time Series in Healthcare

Futoma, J. (2018) · Cited by 8

Optimizing for Interpretability in Deep Neural Networks with Tree RegularizationPreprint

Wu, M., Parbhoo, S., Hughes, M. C., Röth, V., & Doshi‐Velez, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.05254 · Cited by 8

Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for InterpretationPreprint

Wang, S., Wang, Q., & Zhao, J. (2018) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1812.04528 · Cited by 10

May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability

Zhang, T., Yang, X. J., & Li, B. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2364986 · Cited by 12

Exploring Distantly-Labeled Rationales in Neural Network Models

Huang, Q., Zhu, S., Feng, Y., & Zhao, D. (2021) · doi:10.18653/v1/2021.acl-long.433 · Cited by 12

Explaining Semantics and Extension Membership in Abstract Argumentation

Besnard, P., Doutre, S., Duchatelle, T., & Lagasquie-Schiex, M.-C. (2022) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2022.200118 · Cited by 7

Interpretable Neuron Structuring with Graph Spectral Regularization

Tong, A., van Dijk, D., Stanley, J. S., Amodio, M., Yim, K., Muhle, R., Noonan, J. P., Wolf, G., & Krishnaswamy, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-44584-3_40 · Cited by 8

Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience

Chrysostomou, G., & Αλέτρας, Ν. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.645 · Cited by 9

Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection

Bose, T., Αλέτρας, Ν., Illina, I., & Fohr, D. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.32 · Cited by 8

Learning to restore multiple image degradations simultaneously

Zhang, L., Bronik, K., & Papież, B. W. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109250 · Cited by 7

Preemptively pruning Clever-Hans strategies in deep neural networks

Linhardt, L., Müller, K., & Montavon, G. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102094 · Cited by 10

"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to UsersPreprint

Teso, S., & Kersting, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.08578 · Cited by 10

Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis SystemsPreprint

Lucieri, A., Bajwa, M. N., Dengel, A. R., & Ahmed, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13169 · Cited by 20

Language Models have a Moral Dimension.Preprint

Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kristian Kersting, B. (2021) · arXiv (Cornell University) · Cited by 7

Practical Machine Learning Safety: A Survey and PrimerPreprint

Mohseni, S., Wang, H., Yu, Z., Xiao, C., Wang, Z., & Yadawa, J. (2021) · arXiv (Cornell University) · Cited by 10

Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned FeaturesPreprint

Wang, H., Huang, Z., Zhang, H., Lee, Y. J., & Xing, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03740 · Cited by 7

Improving Interpretability via Explicit Word Interaction Graph Layer

Sekhon, A., Chen, H., Shrivastava, A., Wang, Z., Ji, Y., & Qi, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26586 · Cited by 7

Ensuring medical AI safety: interpretability-driven detection and mitigation of spurious model behavior and associated data

Pahde, F., Wiegand, T., Lapuschkin, S., & Samek, W. (2025) · Machine Learning · doi:10.1007/s10994-025-06834-w · Cited by 15

Leveraging explainability for understanding object descriptions in ambiguous 3D environments

Doğan, F. I., Melsión, G. I., & Leite, I. (2023) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.937772 · Cited by 11

The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research

Decker, T. W., Gross, R., Koebler, A., Lebacher, M., Schnitzer, R., & Weber, S. H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35891-3_13 · Cited by 7

ER-Test: Evaluating Explanation Regularization Methods for Language Models

Joshi, B., Chan, A., Liu, Z., Nie, S., Sanjabi, M., Firooz, H., & Ren, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.242 · Cited by 12

Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision

Mustafa, Y., & Luo, T. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2259-4_6 · Cited by 6

How rationals boost textual entailment modeling: Insights from large language models

Pham, D.-H., Le, T., & Nguyen, H. T. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109517 · Cited by 7

Explainable Models with Consistent Interpretations

Pillai, V., & Pirsiavash, H. (2021) · Maryland Shared Open Access Repository (USMAI Consortium) · doi:10.13016/m2orpw-45bj · Cited by 6

Hybrid Decision Making: When Interpretable Models Collaborate With Black-Box Models.Preprint

Wang, T. (2018) · arXiv (Cornell University) · Cited by 6

Robust Semantic Interpretability: Revisiting Concept Activation VectorsPreprint

Pfau, J., Young, A. T., Wei, J., Wei, M. L., & Keiser, M. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.02768 · Cited by 13

DLIME: A Deterministic Local Interpretable Model - Agnostic Explanations Approach for Computer-Aided Diagnosis SystemsPreprint

Zafar, M. R., & Khan, N. (2023) · doi:10.32920/22734359.v1 · Cited by 6

Contextualized race and ethnicity annotations for clinical text from MIMIC-III

Walk, O. J. B. D., Pichon, A., Nieva, H. R., Sun, T., Li, J., Joseph, J. W., Kinberg, S., Richter, L. R., Crusco, S., Kulas, K., et al. (2024) · Scientific Data · doi:10.1038/s41597-024-04183-2 · Cited by 6

Scalable SHAP-Informed Neural Network

Graham, J., & Sheng, V. S. (2025) · Mathematics · doi:10.3390/math13132152 · Cited by 5

Gradient-based explanation for non-linear non-parametric dimensionality reduction

Corbugy, S., Marion, R., & Frénay‬, B. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01055-6 · Cited by 5

SLIM: Spuriousness Mitigation with Minimal Human Annotations

Xuan, X., Deng, Z., Lin, H.-T., & Ma, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72952-2_13 · Cited by 5

Pre Hoc and Co Hoc Explainability: Frameworks for Integrating Interpretability into Machine Learning Training for Enhanced Transparency and Performance

Acun, C., & Nasraoui, O. (2025) · Applied Sciences · doi:10.3390/app15137544 · Cited by 7

Towards Understanding and Arguing with Classifiers: Recent Progress

Shao, X., Rienstra, T., Thimm, M., & Kristian Kersting, B. (2020) · Datenbank-Spektrum · doi:10.1007/s13222-020-00351-x · Cited by 5

Not All Neighbors are Friendly

Maurya, S. K., Liu, X., & Murata, T. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557543 · Cited by 5

To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition

Shen, Q., Teso, S., Giunchiglia, F., & Xu, H. (2023) · Electronics · doi:10.3390/electronics12102275 · Cited by 7

Challenges Towards Production-Ready Explainable Machine Learning

Veiber, L., Allix, K., Arslan, Y., Bissyandé, T. F., & Klein, J. (2020) · Open Repository and Bibliography (University of Luxembourg) · Cited by 5

Efficient Explanations from Empirical Explainers

Schwarzenberg, R., Feldhus, N., & Möller, S. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.17 · Cited by 13

Two to Trust: AutoML for Safe Modelling and Interpretable Deep Learning for Robustness

Amirian, M., Tuggener, L., Chavarriaga, R., Satyawan, Y. P., Schilling, F.-P., Schwenker, F., & Stadelmann, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73959-1_23 · Cited by 9

Measuring Faithful and Plausible Visual Grounding in VQA

Reich, D., Putze, F., & Schultz, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.206 · Cited by 8

Provisioning Robust and Interpretable AI/ML-Based Service Bundles

Preece, A. D., Harborne, D., Raghavendra, R., Tomsett, R., & Braines, D. (2018) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom.2018.8599838 · Cited by 5

Gaining Free or Low-Cost Transparency with Interpretable Partial SubstitutePreprint

Wang, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04346 · Cited by 5

Counterfactual Generation and Fairness Evaluation Using Adversarially Learned InferencePreprint

Dash, S., & Sharma, A. K. (2020) · arXiv (Cornell University) · Cited by 5

Addressing Fairness, Bias and Class Imbalance in Machine Learning: the FBI-lossPreprint

Ferrari, E., & Bacciu, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06345 · Cited by 10

Leveraging Explainability for Comprehending Referring Expressions in the Real WorldPreprint

Doğan, F. I., Melsión, G. I., & Leite, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05593 · Cited by 5

Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions

Shao, X., Skryagin, A., Stammer, W., Schramowski, P., & Kristian Kersting, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i11.17148 · Cited by 20

Deep Contrastive Explanation

Feghahati, S. (2020) · eScholarship (California Digital Library)

Boundary Attributions Provide Normal (Vector) Explanations.Preprint

Wang, Z., Fredrikson, M., & Datta, A. (2021) · arXiv (Cornell University) · Cited by 1

Controllably Sparse Perturbations of Robust Classifiers for Explaining Predictions and Probing Learned Concepts

Roberts, J., & Tsiligkaridis, T. (2021) · Eurographics · doi:10.2312/mlvis.20211072

Robust Models Are More Interpretable Because Attributions Look NormalPreprint

Wang, Z., Fredrikson, M., & Datta, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.11257 · Cited by 14