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

Very Deep Convolutional Networks for Large-Scale Image Recognition

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Adam: A Method for Stochastic Optimization

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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

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Microsoft COCO: Common Objects in Context

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

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"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

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762

Interpretable machine learning: Fundamental principles and 10 grand challenges

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958

Improving performance of deep learning models with axiomatic attribution priors and expected gradients

Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. M., & Lee, S.-I. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00343-w · Cited by 300

Large pre-trained language models contain human-like biases of what is right and wrong to do

Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00458-8 · Cited by 291

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283

A Review on Explainability in Multimodal Deep Neural Nets

Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204

From attribution maps to human-understandable explanations through Concept Relevance Propagation

Achtibat, R., Dreyer, M., Eisenbraun, I., Bosse, S., Wiegand, T., Samek, W., & Lapuschkin, S. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00711-8 · Cited by 181

Explainable Generative AI (GenXAI): a survey, conceptualization, and research agenda

Schneider, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10916-x · Cited by 214

A case-based interpretable deep learning model for classification of mass lesions in digital mammography

Barnett, A. J., Schwartz, F. R., Tao, C., Chen, C., Ren, Y., Lo, J. Y.-C., & Rudin, C. D. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00423-x · Cited by 175

Concepts and applications of digital twins in healthcare and medicine

Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 185

Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning

Xu, R., Cao, J., Feng, G., Luo, J., Feng, Q., Ni, B., & Fang, F. (2021) · Chemical Engineering Journal · doi:10.1016/j.cej.2021.132893 · Cited by 146

Beyond explaining: Opportunities and challenges of XAI-based model improvement

Weber, L., Lapuschkin, S., Binder, A., & Samek, W. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.013 · Cited by 168

Can language models learn from explanations in context?

Lampinen, A., Dasgupta, I., Chan, S., Mathewson, K., Tessler, M., Creswell, A., McClelland, J., Wang, J., & Hill, F. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.38 · Cited by 142

Finding and removing Clever Hans: Using explanation methods to debug and improve deep models

Anders, C. J., Weber, L., Neumann, D., Samek, W., Müller, K., & Lapuschkin, S. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.015 · Cited by 145

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

He, G., Kuiper, L., & Gadiraju, U. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581025 · Cited by 107

Explainable deep learning in plant phenotyping

Mostafa, S., Mondal, D., Panjvani, K., Kochian, L. V., & Stavness, I. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1203546 · Cited by 99

EnD: Entangling and Disentangling deep representations for bias correction

Tartaglione, E., Barbano, C. A., & Grangetto, M. (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/cvpr46437.2021.01330 · Cited by 92

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

Garrett, K. A., Bebber, D. P., Etherton, B. A., Gold, K. M., Sulá, A. I. P., & Selvaraj, M. G. (2022) · Annual Review of Phytopathology · doi:10.1146/annurev-phyto-021021-042636 · Cited by 80

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

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

Gao, Y., Gu, S., Jiang, J., Hong, S.-S., Yu, D., & Zhao, L. (2024) · ACM Computing Surveys · doi:10.1145/3644073 · Cited by 85

Machine vision combined with deep learning–based approaches for food authentication: An integrative review and new insights

Shen, C., Wang, R., Nawazish, H., Wang, B., Cai, K., & Xu, B. (2024) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.70054 · Cited by 75

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. (2023) · F1000Research · doi:10.12688/f1000research.52204.2 · Cited by 52

Leveraging explanations in interactive machine learning: An overview

Teso, S., Alkan, Ö., Stammer, W., & Daly, E. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1066049 · Cited by 76

Explanation-Based Human Debugging of NLP Models: A Survey

Lertvittayakumjorn, P., & Toni, F. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00440 · Cited by 61

Visualization for Recommendation Explainability: A Survey and New Perspectives

Chatti, M. A., Guesmi, M., & Muslim, A. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3672276 · Cited by 56

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

Mahlein, A., Barbedo, J. G. A., Chiang, K., Ponte, E. M. D., & Bock, C. H. (2024) · Phytopathology · doi:10.1094/phyto-01-24-0009-per · Cited by 53

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

Salimzadeh, S., He, G., & Gadiraju, U. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641905 · Cited by 41

Potentials and caveats of AI in hybrid imaging

Sundar, L. K. S., Muzik, O., Buvat, I., Bidaut, L. M., & Beyer, T. (2020) · Methods · doi:10.1016/j.ymeth.2020.10.004 · Cited by 40

Digital plant pathology: a foundation and guide to modern agriculture

Kuśka, M. T., Heim, R. H. J., Geedicke, I., Gold, K. M., Brugger, A., & Paulus, S. (2022) · Journal of Plant Diseases and Protection · doi:10.1007/s41348-022-00600-z · Cited by 46

EXMOS: Explanatory Model Steering through Multifaceted Explanations and Data Configurations

Bhattacharya, A., Stumpf, S., Gosak, L., Štiglic, G., & Verbert, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642106 · Cited by 47

Explainable AI reveals Clever Hans effects in unsupervised learning models

Kauffmann, J., Dippel, J., Ruff, L., Samek, W., Müller, K., & Montavon, G. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01000-2 · Cited by 50

Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision

Yan, S., Yu, Z. T., Zhang, X., Mahapatra, D., Chandra, S. S., Janda, M., Soyer, H. P., & Ge, Z. (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/cvpr52729.2023.01113 · Cited by 36

An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning

Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., & Welke, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43418-1_28 · Cited by 35

Adversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans

Vadillo, J., Santana, R., & Lozano, J. A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1567 · Cited by 42

A graph-based interpretability method for deep neural networks

Wang, T., Zheng, X., Zhang, L., Cui, Z., & Xu, C. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126651 · Cited by 38

A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?

Wang, Z., Huang, C., & Yao, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3702004 · Cited by 38

Explainable Machine Learning with Prior Knowledge: An Overview

Beckh, K., Müller, S., Jakobs, M., Toborek, V., Tan, H., Fischer, R., Welke, P., Houben, S., & von Rueden, L. (2021) · Lancaster EPrints (Lancaster University) · doi:10.48550/arxiv.2105.10172 · Cited by 52

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

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 34

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

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

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 38

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

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

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

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

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 32

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

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 27

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

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

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 27

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

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

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 23

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

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

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 23

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

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 20

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 20

Decision Boundary Visualization for Counterfactual Reasoning

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

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 23

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

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 21

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 17

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

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

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 17

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

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

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

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

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

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

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

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

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

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

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

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 18

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

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

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

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