Making deep neural networks right for the right scientific reasons by interacting with their explanations

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

Adam: A Method for Stochastic Optimization

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

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460

Untitled work

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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513

"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

Learning Deep Features for Discriminative Localization

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428

A tutorial on spectral clustering

Ulrike Von Luxburg, A. (2007) · Statistics and Computing · doi:10.1007/s11222-007-9033-z · Cited by 10,787

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

Variant Approach for Identifying Spurious Relations That Deep Learning Models Learn

Tesch, T., Kollet, S. J., & Garcke, J. (2021) · Frontiers in Water · doi:10.3389/frwa.2021.745563 · Cited by 7

LIMEADE: From AI Explanations to Advice Taking

Lee, B. C. G., Downey, D., Lo, K., & Weld, D. S. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3589345 · Cited by 13

A MLOps Architecture for XAI in Industrial Applications

Faubel, L., Woudsma, T., Methnani, L., Ghezeljehmeidan, A. G., Buelow, F., Schmid, K., van Driel, W. D., Kloepper, B., Theodorou, A. A., Nosratinia, M., et al. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10711084 · Cited by 7

Data Mining System Architecture for Industrial Internet of Things in Electronics Production

Seidel, R., Amada, H., Fuchs, J., Thielen, N., Schmidt, K., Voigt, C., & Franke, J. E. (2020) · IEEE International Symposium for Design and Technology in Electronic Packaging (SIITME) · doi:10.1109/siitme50350.2020.9292282 · Cited by 7

DeepVir: Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning—Application to COVID-19

Mongia, A., Jain, S., Chouzenoux, É., & Majumdar, A. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0108 · Cited by 8

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

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

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

Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systems

Bhattacharya, A., Stumpf, S., Croon, R. D., & Verbert, K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713497 · Cited by 16

Brain-inspired dual-pathway neural network architecture and its generalization analysis

Dong, S., Tan, C., Zuo, Z., He, Y., Gong, Y., Zhou, T., Liu, J., & Zhang, J. (2024) · Science China Technological Sciences · doi:10.1007/s11431-024-2753-3 · Cited by 6

PhysioEx: a new Python library for explainable sleep staging through deep learning

Gagliardi, G., Alfeo, A. L., Cimino, M. G. C. A., Valenza, G., & Vos, M. D. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adaf73 · Cited by 6

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

Interactively Providing Explanations for Transformer Language Models

Friedrich, F., Schramowski, P., Tauchmann, C., & Kristian Kersting, B. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220218 · Cited by 6

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

Neutron penumbral image reconstruction with a convolution neural network using fast Fourier transform

Song, J., Zheng, J., Chen, Z., Chen, J., & Wang, F. (2024) · Review of Scientific Instruments · doi:10.1063/5.0175347 · 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

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

WeatherPon: A Weather and Machine Learning-Based Coupon Recommendation Mechanism in Digital Marketing

Liu, L., & Han, M. (2023) · IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI) · doi:10.1109/seai59139.2023.10217467 · Cited by 5

PT-Finder: A multi-modal neural network approach to target identification

Nada, H., Kim, S.-D., & Lee, K. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108444 · Cited by 5

Human-in-the-loop handling of knowledge drift

Bontempelli, A., Giunchiglia, F., Passerini, A., & Teso, S. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00845-0 · 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

Integrating human knowledge for explainable AI

Cappuccio, E., Kathirgamanathan, B., Rinzivillo, S., Andrienko, G., & Andrienko, N. V. (2025) · Machine Learning · doi:10.1007/s10994-025-06879-x · Cited by 4

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

Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances

Penzel, N., Kierdorf, J., Roscher, R., & Denzler, J. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00064 · 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

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

Knowledge-based XAI through CBR: There is more to explanations than models can tellPreprint

Weber, R. O., Shrestha, M., & Johs, A. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10363 · 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

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

A Whale’s Tail - Finding the Right Whale in an Uncertain World

Marcos, D., Kierdorf, J., Cheeseman, T., Tuia, D., & Roscher, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_15 · Cited by 3

Establishing Trustworthiness: Rethinking Tasks and Model Evaluation

Litschko, R., Müller-Eberstein, M., van der Goot, R., Weber, L., & Plank, B. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.14 · Cited by 7

Bayesian CAIPI: A Probabilistic Approach to Explanatory and Interactive Machine Learning

Slany, E., Scheele, S., & Schmid, U. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50396-2_16 · Cited by 3

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

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

Interactive Explainable Anomaly Detection for Industrial Settings

Gramelt, D., Höfer, T., & Schmid, U. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-92805-5_9 · Cited by 4

Adaptive Echo State Network for crop yield prediction incorporating Fall Armyworm dynamics

Chibuye, M., Phiri, J., & Nkunika, P. O. Y. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101123 · Cited by 2

Explainable AI for Visual Inspection: A Comparative Analysis and Review

Albydhany, M. H., & Al‐Darraji, I. (2025) · Journal of Visualized Experiments · doi:10.3791/69440 · Cited by 3

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

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

Interactive deep learning for explainable retinal disease classification

Vasquez, M., Shakya, S., Wang, I., Furst, J. D., Tchoua, R., & Raicu, D. S. (2022) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2611822 · 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

LIFEDATA - A Framework for Traceable Active Learning Projects

Stieler, F., Elia, M., Weigell, B., Bauer, B., Kienle, P., Roth, A., Müllegger, G., Nann, M., & Dopfer, S. (2023) · IEEE International Requirements Engineering Conference Workshops (REW) · doi:10.1109/rew57809.2023.00088 · 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

Explainable AI in Manufacturing

Zeiser, T., Ehret, D., Lutz, T., & Saar, J. (2024) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc61926.2024.10794363 · Cited by 6

Spatial-Spectral feature extraction of hyperspectral images for tongue diagnosis digitalization and classification

Chen, Y., Zhang, H., Dong, Z., Tan, Q., Yang, Z., & Wang, C. (2025) · Measurement · doi:10.1016/j.measurement.2025.117525 · 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

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

Adversarial training and attribution methods enable evaluation of robustness and interpretability of deep learning models for image classification

Santos, F., Zanchettin, C., Lei, W., & Amaral, L. A. N. (2024) · Physical review. E · doi:10.1103/physreve.110.054310 · 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

Deep Probabilistic Matrix Factorization on Graphs: Application to Drug Repositioning in Antimicrobial Resistance

Chatterjee, S., Jain, S., Kumar, K., Chouzenoux, É., & Majumdar, A. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3570865 · Cited by 1

Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers

Tung, L. N., Cho, S. B., Du, X., Neelofar, N., Terragni, V., Ruberto, S., & Aleti, A. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3729376 · Cited by 6

Künstliche Intelligenz auf dem Prüfstand: Anforderungen, Qualitätskriterien und Prüfwerkzeuge für medizinische Anwendungen

Ma, J., Weicken, E., Pahde, F., Weitz, K., Lapuschkin, S., Samek, W., & Wiegand, T. (2025) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-025-04101-w · Cited by 1

Agrivision: Hybrid Deep Learning Models for Enhanced Plant Disease Detection

Edraoui, Y., Es-Saidi, S., Rahda, A., Chtouki, Y., & Ahmad, I. (2025) · IEEE International Conference on Networking, Intelligent Systems, and IoT (ICONS-IoT) · doi:10.1109/icons-iot65216.2025.11210958 · Cited by 1

CorrectMe: An Interactive Framework for Human-in-the-Loop Correction and Explanation of Object Detection Models

Liu, Y., Wu, Z., Dong, X., Jiang, S., & Li, W. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3773065 · Cited by 1

UntrustVul: Automated Untrustworthy Alert Identification in Vulnerability Detection Models

Tung, L. N., Du, X., Neelofar, N., & Aleti, A. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3693636 · Cited by 1

MODELING AND ANALYSIS OF THE BOOK BORROWING OF STUDENTS IN THE LIBRARY USING PARTIAL DIFFERENTIAL EQUATIONS

WANG, J., Lin, Z., Alsulami, H. H., & Chen, J. (2021) · Fractals · doi:10.1142/s0218348x22400709 · Cited by 1

Unlearning Spurious Correlations in Chest X-Ray Classification

Hagos, M. T., Curran, K. M., & Namee, B. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45275-8_26 · Cited by 1

Learning Quantum System Disturbance Models with Probabilistic Bayesian Neural Networks

Jiang, Z., & Nguyen, L. (2023) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon58068.2023.10365822 · Cited by 1

Targeted Activation Penalties Help CNNs Ignore Spurious Signals

Zhang, D., Williams, M., & Toni, F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29610 · Cited by 1

Hybrid Explanatory Interactive Machine Learning for Medical Diagnosis

Slany, E., Scheele, S., & Schmid, U. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63211-2_9 · Cited by 1

QuProbLeM - A Novel Quantum Probabilistic Learning Machine: Software Architecture

Jiang, Z., & Nguyen, L. (2024) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon61878.2024.10670619 · Cited by 1

Aligning Bytes with Bliss: Integrating Happiness Computing with Sociological Insight

Wu, X., Li, L., Tao, X., & Li, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0811-9_13 · Cited by 1

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19Preprint

Majumdar, A., Mongia, A., Chouzenoux, É., & Jain, S. (2020) · doi:10.36227/techrxiv.12987497.v1 · Cited by 1

Debiased-CAM for bias-agnostic faithful visual explanations of deep convolutional networks.Preprint

Zhang, W., Dimiccoli, M., & Lim, B. Y. (2020) · arXiv (Cornell University) · Cited by 1

Alfie: An Interactive Robot with Moral Compass

Turan, C., Schramowski, P., Rothkopf, C. A., & Kersting, K. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3421163

A design of human-like robust AI machines in object identificationPreprint

Hu, B.-G., & Dong, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02327

Towards Participatory Design Spaces for Explainable AI Interfaces in Expert Domains (Short Paper).

Mucha, H., Robert, S., Breitschwerdt, R., & Michael Fellmann, M. A. (2020)

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs.Preprint

Luo, L., Chen, H., Xiao, Y., Zhou, Y., Wang, X., Vardhanabhuti, V., Wu, M., & Heng, P. (2021) · arXiv (Cornell University)

Co-creating Platformer Levels with Constrained Adversarial Networks.

Morettin, P., Passerini, A., & Teso, S. (2021) · Institutional Research Information System (Università degli Studi di Trento)

Interactively Generating Explanations for Transformer Language Models.Preprint

Schramowski, P., Friedrich, F., Tauchmann, C., & Kersting, K. (2021) · arXiv (Cornell University)

LIMEADE: A General Framework for Explanation-Based Human Tuning of Opaque Machine Learners.Preprint

Lee, B. C. G., Downey, D., Lo, K., & Weld, D. S. (2021) · arXiv (Cornell University)

DeepVir - Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning: Application to COVID-19Preprint

Majumdar, A., Mongia, A., Chouzenoux, É., & Jain, S. (2020) · doi:10.36227/techrxiv.12987497

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Discovering Rule Lists with Preferred Variables

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Preemptively Pruning Clever-Hans Strategies in Deep Neural NetworksPreprint

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Embedding-Space Explanations of Learned Mixture Behavior

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Neural Networks as a Means to Study the Factors Having Influence on Adjusted Net Savings

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Exploring Image Classification Robustness and Interpretability with Right for the Right Reasons Data Augmentation

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ALLFA: Active Learning Through Label and Feature Augmentation

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Visualization Analysis of Convolutional Neural Network Processes

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Model Guidance via Explanations Turns Image Classifiers into Segmentation Models

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ESC: Explaining the Predictions of Deep Neural Networks via Concept Similarity

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Effective Guidance for Model Attention with Simple Yes-no Annotations

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Granular Feedback: Leveraging Domain Expertise and Explainable AI to Effectively Steer Models

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Explainable Artificial Intelligence: State of the Art and Beyond

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Explaining and Interpreting Generative AI

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TEXTWISE: Text Exploration through Interactive Natural Language Processing for Wide-Ranging Insights and Semantic Exploration

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Enhancing concept alignment with explanatory interactive disentangled representation learning

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Trustworthy AI-based crack-tip segmentation using domain-guided explanations

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Interpretable classification of endomicroscopic brain data via saliency consistent contrastive learning

Xu, C., Roddan, A., Kakaletri, I., Charalampaki, P., & Giannarou, S. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103917

Towards a Standardized Interactive AI Transparency Label

Goram, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99958-1_34

Hybrid Explanation-Guided Learning for Transformer-Based Chest X-Ray Diagnosis

Shu, S. Z., Luo, H., Poellinger, A., & Reyes, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-17611-0_4

Industrial-Scale Autonomous eXplainable Iterative Learning

Strem, N., Dhami, D. S., & Kristian Kersting, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7081-2_12

Interactive explainability in data-driven modeling: A neural-network-centric survey

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