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

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Sanity Checks for Saliency Maps

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A Benchmark for Interpretability Methods in Deep Neural NetworksPreprint

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Interpreting Blackbox Models via Model ExtractionPreprint

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Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems

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Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

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Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning

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Explanation-Based Human Debugging of NLP Models: A Survey

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Visualization for Recommendation Explainability: A Survey and New Perspectives

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From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management

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Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making

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Happiness Prediction With Domain Knowledge Integration and Explanation Consistency

Wu, X., Li, L., Tao, X., Xing, F., & Yuan, J. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3529946 · Cited by 29 · see citing works

Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning

Mumuni, F., & Mumuni, A. (2023) · Cognitive Systems Research · doi:10.1016/j.cogsys.2023.101188 · Cited by 28 · see citing works

Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset

Stadtler, S., Betancourt, C., & Roscher, R. (2022) · Machine Learning and Knowledge Extraction · doi:10.3390/make4010008 · Cited by 28 · see citing works

Deep Neural Networks Based Approach for Battery Life Prediction

Bhattacharya, S., Maddikunta, P. K. R., Iyapparaja, M., Gadekallu, T. R., Sharma, S., Alkahtani, M. S., & Abidi, M. H. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016229 · Cited by 28 · see citing works

Explanatory Interactive Machine Learning

Pfeuffer, N., Baum, L., Stammer, W., Abdel-Karim, B. M., Schramowski, P., Bucher, A. M., Hügel, C., Rohde, G. G. U., Kersting, K., & Hinz, O. F. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00806-x · Cited by 30 · see citing works

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations

Dreyer, M., Achtibat, R., Wiegand, T., Samek, W., & Lapuschkin, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00397 · Cited by 28 · see citing works

Climate Change Impact on Plant Pathogen Emergence: Artificial Intelligence (AI) Approach

Ali, F., Rehman, A., Hameed, A., Sarfraz, S., Rajput, N. A., & Atiq, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-56011-8_9 · Cited by 26 · see citing works

Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction

Brugger, A., Schramowski, P., Paulus, S., Steiner, U., Kersting, K., & Mahlein, A. (2021) · Plant Pathology · doi:10.1111/ppa.13411 · Cited by 29 · see citing works

Toward False Event Detection and Quarry Blast versus Earthquake Discrimination in an Operational Setting Using Semiautomated Machine Learning

Renouard, A., Maggi, A., Grunberg, M., Doubre, C., & Hibert, C. (2021) · Seismological Research Letters · doi:10.1785/0220200305 · Cited by 26 · see citing works

Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning–based Radiograph Diagnosis: A Multicenter Study

Luo, L., Chen, H., Xiao, Y., Zhou, Y., Wang, X., Vardhanabhuti, V., Wu, M., Han, C., Liu, Z., Fang, B., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210299 · Cited by 29 · see citing works

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites

Brugger, A., Yamati, F. R. I., Barreto, A., Paulus, S., Schramowsk, P., Kristian Kersting, B., Steiner, U., Neugart, S., & Mahlein, A. (2022) · Phytopathology · doi:10.1094/phyto-03-22-0086-r · Cited by 26 · see citing works

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Xu, R., Cao, J.-S., Luo, J., Ni, B., Fang, F., Liu, W., & Wang, P. (2024) · npj Clean Water · doi:10.1038/s41545-024-00367-w · Cited by 25 · see citing works

Data management challenges for artificial intelligence in plant and agricultural research

Williamson, H. F., Brettschneider, J. A., Cáccamo, M., Davey, R., Goble, C., Kersey, P. J., May, S. T., Morris, R. J., Ostler, R., Pridmore, T., et al. (2021) · F1000Research · doi:10.12688/f1000research.52204.1 · Cited by 24 · see citing works

Improved detection and identification approach in tomato leaf disease using transformation and combination of transfer learning features

Djimeli-Tsajio, A., Noulamo, T., Jean-Pierre, L. T., Kapche, T. F., & Nagabhushan, P. N. (2022) · Journal of Plant Diseases and Protection · doi:10.1007/s41348-022-00608-5 · Cited by 23 · see citing works

ICICLE: Interpretable Class Incremental Continual Learning

Rymarczyk, D., van de Weijer, J., Zieliński, B., & Twardowski, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00181 · Cited by 25 · see citing works

Systematic biases when using deep neural networks for annotating large catalogs of astronomical images

Dhar, S., & Shamir, L. (2022) · Astronomy and Computing · doi:10.1016/j.ascom.2022.100545 · Cited by 23 · see citing works

Prompt-Driven Latent Domain Generalization for Medical Image Classification

Yan, S., Yu, Z. T., Liu, C., Ju, L., Mahapatra, D., Betz‐Stablein, B., Mar, V., Janda, M., Soyer, H. P., & Ge, Z. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3443119 · Cited by 22 · see citing works

Harnessing Prior Knowledge for Explainable Machine Learning: An Overview

Beckh, K., Müller, S., Jakobs, M., Toborek, V., Tan, H., Fischer, R., Welke, P., Houben, S., & von Rueden, L. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00038 · Cited by 22 · see citing works

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

Pahde, F., Dreyer, M., Samek, W., & Lapuschkin, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_56 · Cited by 21 · see citing works

Multiview Shapelet Prototypical Network for Few-Shot Fault Incremental Learning

Wan, X., Cen, L., Chen, X., Xie, Y., & Gui, W. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3413304 · Cited by 20 · see citing works

Towards Guidelines for Designing Human-in-the-Loop Machine Training Interfaces

van der Stappen, A., & Funk, M. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450668 · Cited by 20 · see citing works

Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations

Stammer, W., Memmel, M., Schramowski, P., & Kristian Kersting, B. (2022) · 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/cvpr52688.2022.01007 · Cited by 19 · see citing works

Explainable deep learning: concepts, methods, and new developments

Samek, W. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-396098-4.00008-9 · Cited by 19 · see citing works

Pseudo-class part prototype networks for interpretable breast cancer classification

Choukali, M. A., Amirani, M. C., Valizadeh, M., Abbasi, A., & Komeili, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-60743-x · Cited by 18 · see citing works

Decision Boundary Visualization for Counterfactual Reasoning

Sohns, J., Garth, C., & Leitte, H. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14650 · Cited by 17 · see citing works

A typology for exploring the mitigation of shortcut behaviour

Friedrich, F., Stammer, W., Schramowski, P., & Kersting, K. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00612-w · Cited by 18 · see citing works

GranDIHC-BR 2025-2035 - GC5 - Human-Data Interaction Data Literacy and Usable Privacy

Coleti, T. A., Divino, S. B. S., Salgado, A. D. L., Zacarias, R. O., Saraiva, J. D. A. G., Gonçalves, D. A., Morandini, M., & dos Santos, R. P. (2024) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3702038.3702058 · Cited by 17 · see citing works

To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems

He, G., Bharos, A., & Gadiraju, U. (2024) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3648188.3675130 · Cited by 17 · see citing works

Artificial intelligence redefines agricultural genetics by unlocking the enigma of genomic complexity

Dublino, R., & Ercolano, M. R. (2025) · The Crop Journal · doi:10.1016/j.cj.2025.05.008 · Cited by 16 · see citing works

Artificial Intelligence You Can Trust: What Matters Beyond Performance When Applying Artificial Intelligence to Renal Histopathology?

Ayorinde, J., Citterio, F., Landrò, M., Peruzzo, E., Islam, T., Tilley, S., Taylor, G., Bardsley, V., Lió, P., Samoshkin, A., et al. (2022) · Journal of the American Society of Nephrology · doi:10.1681/asn.2022010069 · Cited by 16 · see citing works

Navigating the landscape of concept-supported XAI: Challenges, innovations, and future directions

Khoozani, Z. S., Sabri, A. Q. M., Seng, W. C., Seera, M., & Eg, K. Y. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17666-y · Cited by 18 · see citing works

Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty

Yang, J., Sun, Z., Tian, S., Jiang, H., Feng, J., Ting, K. C., Lin, T., & Ying, Y. B. (2024) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2024.113009 · Cited by 16 · see citing works

Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations

Dreyer, M., Achtibat, R., Samek, W., & Lapuschkin, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00353 · Cited by 16 · see citing works

CAIPI in Practice: Towards Explainable Interactive Medical Image Classification

Slany, E., Ott, Y., Scheele, S., Paulus, J., & Schmid, U. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-08341-9_31 · Cited by 16 · see citing works

A special issue on phytopathometry — visual assessment, remote sensing, and artificial intelligence in the twenty-first century

Bock, C. H., Barbedo, J. G. A., Mahlein, A., & Ponte, E. M. D. (2022) · Tropical Plant Pathology · doi:10.1007/s40858-022-00498-w · Cited by 16 · see citing works

Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning Reveals the Giant Exoplanet AF Lep b in High-contrast Imaging Data from 2011

Bonse, M. J., Gebhard, T. D., Dannert, F., Absil, O., Cantalloube, F., Christiaens, V., Cugno, G., Garvin, E. O., Hayoz, J., Kasper, M. E., et al. (2025) · The Astronomical Journal · doi:10.3847/1538-3881/adab79 · Cited by 14 · see citing works

Enterprise imaging and big data: A review from a medical physics perspective

McCarthy, N., Dahlan, A., Cook, T. S., Hare, N. O., Ryan, M.-L., John, B. S., Lawlor, A., & Curran, K. M. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.04.004 · Cited by 14 · see citing works

Improve the Deep Learning Models in Forestry Based on Explanations and Expertise

Cheng, X., Doosthosseini, A., & Kunkel, J. M. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.902105 · Cited by 14 · see citing works

Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning

Yamati, F. R. I., Günder, M., Barreto, A., Bömer, J., Laufer, D., Bauckhage, C., & Mahlein, A. (2023) · Plant Disease · doi:10.1094/pdis-04-23-0779-re · Cited by 13 · see citing works

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 · see citing works

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 15 · see citing works

Interactive Label Cleaning with Example-based ExplanationsPreprint

Teso, S., Bontempelli, A., Giunchiglia, F., & Passerini, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.03922 · Cited by 13 · see citing works

Don’t Be Fooled: The Misinformation Effect of Explanations in Human–AI Collaboration

Spitzer, P., Holstein, J., Morrison, K., Holstein, K., Satzger, G., & Kühl, N. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2574511 · Cited by 12 · see citing works

Towards symbolic XAI — explanation through human understandable logical relationships between features

Schnake, T., Jafari, F. R., Lederer, J., Xiong, P., Nakajima, S., Gugler, S., Montavon, G., & Müller, K. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102923 · Cited by 12 · see citing works

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 12 · see citing works

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital MammographyPreprint

Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y.-C., & Rudin, C. D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12308 · Cited by 12 · see citing works

Reliability Scores From Saliency Map Clusters for Improved Image-Based Harvest-Readiness Prediction in Cauliflower

Kierdorf, J., & Roscher, R. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3293802 · Cited by 11 · see citing works

Interpretability Analysis of Convolutional Neural Networks for Crack Detection

Wu, J., He, Y., Xu, C., Jia, X., Huang, Y., Chen, Q., Huang, C., Eslamlou, A. D., & Huang, S. (2023) · Buildings · doi:10.3390/buildings13123095 · Cited by 12 · see citing works

Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions

Bhattacharya, A. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3638177 · Cited by 10 · see citing works

Position Papers for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking

Lougovski, P., Parekh, O., Broz, J., Byrd, M., Chembo, Y. K., de Jong, W. A., Figueroa, E. V., Humble, T. S., Larson, J. M., Quiroz, G., et al. (2023) · doi:10.2172/2283320 · Cited by 10 · see citing works

2025 photonics for agrifood roadmap: towards a sustainable and healthier planet

Smeesters, L., Venturini, F., Paulus, S., Mahlein, A., Perpetuini, D., Cardone, D., Merla, A., Gac, B., Perrin, S., Trégoat, D., et al. (2025) · Journal of Physics Photonics · doi:10.1088/2515-7647/adbea9 · Cited by 10 · see citing works

Hierarchical Generative Network: A Hierarchical Multitask Learning Approach for Accelerated Composite Material Design and Discovery

Park, D., Lee, J., Park, K., & Ryu, S. (2023) · Advanced Engineering Materials · doi:10.1002/adem.202300867 · Cited by 8 · see citing works

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 9 · see citing works

Aerial low‐altitude remote sensing and deep learning for in‐field disease incidence scoring of virus yellows in sugar beet

Okole, N., Yamati, F. R. I., Hossain, R., Varrelmann, M., Mahlein, A., & Heim, R. H. J. (2024) · Plant Pathology · doi:10.1111/ppa.13973 · Cited by 9 · see citing works

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

Kolek, S., Nguyễn, Đ. A., Levie, R., Bruna, J., & Kutyniok, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_6 · Cited by 9 · see citing works

An Explanatory Model Steering System for Collaboration between Domain Experts and AI

Bhattacharya, A., Stumpf, S., & Verbert, K. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664886 · Cited by 8 · see citing works

Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review

Debbagh, M., Sun, S., & Lefsrud, M. G. (2025) · Plant Phenomics · doi:10.1016/j.plaphe.2025.100089 · Cited by 8 · see citing works

Enhancing Descriptive Image Quality Assessment With a Large-Scale Multi-Modal Dataset

You, Z., Gu, J., Cai, X., Li, Z., Zhu, K., Dong, C., & Xue, T. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3639998 · Cited by 23 · see citing works

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 8 · see citing works

Representation Debiasing of Generated Data Involving Domain Experts

Bhattacharya, A., Stumpf, S., & Verbert, K. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664910 · Cited by 8 · see citing works

User Driven Model Adjustment via Boolean Rule Explanations

Daly, E., Mattetti, M., Alkan, Ö., & Nair, R. S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i7.16737 · Cited by 8 · see citing works

Impact of High-Cadence Earth Observation in Maize Crop Phenology Classification

Nieto, L., Houborg, R., Zajdband, A. D., Jumpasut, A., Prasad, P. V. V., Olson, B. J. S. C., & Ciampitti, I. A. (2022) · Remote Sensing · doi:10.3390/rs14030469 · Cited by 8 · see citing works

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo, Y., Zou, X., Hu, Y., Yang, Y., Wang, X., He, Y., Kong, R., Guo, Y., Li, G., Zhang, W., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100054 · Cited by 10 · see citing works

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 · see citing works

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 7 · see citing works

Explanatory Interactive Machine Learning

Teso, S., & Kersting, K. (2019) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314293 · Cited by 276 · see citing works

Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations

Stammer, W., Schramowski, P., & Kersting, K. (2021) · 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/cvpr46437.2021.00362 · Cited by 73 · see citing works

Towards More Interpretable And Faithful Explanations In Deep Neural Networks

Kalanovska, S. (2025) · Research Portal (King's College London)