Publications
Bahr, L., Wehner, C., Wewerka, J., Bittencourt, J., Schmid, U., & Daub, R. (2025). Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis. Journal of Industrial Information Integration, 45(100807), 1–9. https://doi.org/10.1016/j.jii.2025.100807
Finzel, B. (2025). Current methods in explainable artificial intelligence and future prospects for integrative physiology. Pflügers Archiv, 477(4), 513–529. https://doi.org/10.1007/s00424-025-03067-7
Poßner, L., Bahr, L., Röhl, L., Wehner, C., & Gröger, S. (2025). Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage. Rethinking Quality - Wandel des Qualitätsmanagements durch Digitalisierung und Künstliche Intelligenz: Bericht zur GQW-Jahrestagung 2024 in Berlin, 39–59. https://doi.org/10.1007/978-3-658-47213-9_3
Ruschhaupt, S., Troles, J., & Schmid, U. (2025). Comparing Mask R-CNN and Mask2Former architectures for individual tree crown delineation. 45. GIL-Jahrestagung, Digitale Infrastrukturen Für Eine Nachhaltige Land-, Forst-Und Ernährungswirtschaft, 167–178. https://doi.org/10.18420/giljt2025_13
Schmid, U., Weitz, K., & Siebers, M. (2025). Künstliche Intelligenz selber programmieren für Dummies Junior: wer ist schlauer? Mensch oder Maschine (2. vollständig überarbeitete und erweiterte Auflage). Wiley-VCH GmbH.
Singla, A., Hobert, S., Joachim, S., Hennecke, M., Voelker, A., Thaler, A. M., Summerer, M. T., Schmid, U., Bayerkuhnlein, M., Wolter, D., Bugert, F., Bittner, D., Ezer, T., Nadimpalli, V. K., Staufer, S., Hauser, F., Maier, R., Grabinger, L., Mottok, J., & Niemann, S. (2025). Proceedings of the Second Work shop on Artificial Intelligence for Artificial Intelligence Education (AI4AI Learning 2024). Second Workshop on Artificial Intelligence for Artificial Intelligence Education (AI4AI Learning 2024), Würzburg. https://doi.org/10.20378/irb-107661
Slany, E. (2025). Enhancing Explanatory Interactive Machine Learning: A Generalization of the CAIPI Algorithm [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-107376
Furbach, U., Kitzelmann, E., Michaeli, T., & Schmid, U. (Eds.). (2024). Künstliche Intelligenz für Lehrkräfte: eine fachliche Einführung mit didaktischen Hinweisen (1st ed.). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-44248-4
Amling, J., Scheele, S., Slany, E., Lang, M., & Schmid, U. (2024). Explainable AI for Mixed Data Clustering. Explainable Artificial Intelligence: Second World Conference, XAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part 2, 42–62. https://doi.org/10.1007/978-3-031-63797-1_3
Atzmueller, M., Fürnkranz, J., Kliegr, T., & Schmid, U. (2024). Explainable and interpretable machine learning and data mining. Data Mining and Knowledge Discovery, 38(5), 2571–2595. https://doi.org/10.1007/s10618-024-01041-y
Fan, W., Tian, J., Troles, J., Döllerer, M., Kindu, M., & Knoke, T. (2024). Comparing Deep Learning and MCWST Approaches for Individual Tree Crown Segmentation. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1–2024, 67–73. https://doi.org/10.5194/isprs-annals-x-1-2024-67-2024
Finzel, B. (2024). Human-Centered Explanations: Lessons Learned from Image Classification for Medical and Clinical Decision Making. Künstliche Intelligenz, 38(3), 157–167. https://doi.org/10.1007/s13218-024-00835-y
Finzel, B., Hilme, P., Rabold, J., & Schmid, U. (2024). When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX (pp. 1–56). arXiv. https://doi.org/10.48550/arxiv.2405.01661
Finzel, B., Knoblach, J., Thaler, A. M., & Schmid, U. (2024). Near Hit and Near Miss Example Explanations for Model Revision in Binary Image Classification. Intelligent Data Engineering and Automated Learning – IDEAL 2024: 25th International Conference, Valencia, Spain, November 20–22, 2024, Proceedings, Part II, 260–271. https://doi.org/10.1007/978-3-031-77738-7_22
Finzel, B., Kuhn, S. P., Tafler, D. E., & Schmid, U. M. (2024). Explaining with Attribute-Based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust. Inductive Logic Programming: 31st International Conference, ILP 2022, Windsor Great Park, UK, September 28–30, 2022, Proceedings, 40–51. https://doi.org/10.1007/978-3-031-55630-2_4
Gärtig-Daugs, A., & Schmid, U. (2024). Informatische Grundbildung in der Grundschule: Kombinierter Einsatz von haptischen Spiel- und Erfahrungsmaterialien mit digitalen Übungen. In B. Brandt & L. Bröll (Eds.), Digitales Lernen in der Grundschule IV: Fachdidaktische Perspektiven auf den Einsatz digitaler Werkzeuge (1st ed., pp. 104–124). Waxmann.
Kohlhase, M., Berges, M., Grubert, J., Henrich, A., Landes, D., Leidner, J. L., Mittag, F., Nicklas, D., Schmid, U., Sedlmaier, Y., Ulbrich-vom Ende, A., & Wolter, D. (2024). Project VoLL-KI: Learning from Learners. Künstliche Intelligenz, Online First, 1–11. https://doi.org/10.1007/s13218-024-00846-9
Motzkus, F. W., Mikriukov, G., Hellert, C., & Schmid, U. (2024). Locally testing model detections for semantic global concepts. Explainable Artificial Intelligence: Second World Conference, XAI 2024, Valletta, Malta, July 17-19, 2024: Proceedings, Part 1, 137–159. https://doi.org/10.1007/978-3-031-63787-2_8
Neufeld, D. (2024). Supporting Experts in Detecting and Interpreting Anomalies in Time Series: Exploring Data Science Approaches for the Monitoring of Hydraulic Test Benches [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-95413
Schmid, U. (2024). Trustworthy Artificial Intelligence: Comprehensible, Transparent and Correctable. In H. Werthner, C. Ghezzi, J. Kramer, J. Nida-Rümelin, B. Nuseibeh, E. Prem, & A. Stanger (Eds.), Introduction to digital humanism: a textbook (pp. 151–164). Springer. https://doi.org/10.1007/978-3-031-45304-5_10
Schmid, U., Weitz, K., & Siebers, M. (2024). Künstliche Intelligenz selber programmieren für Dummies Junior (2nd ed.). Wiley-VCH.
Schramm, S., Wehner, C., & Schmid, U. (2024). Comprehensible Artificial Intelligence on Knowledge Graphs: A survey (pp. 1–23). arXiv. https://doi.org/10.48550/arxiv.2404.03499
Schwalbe, G., & Finzel, B. (2024). A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts. Data Mining and Knowledge Discovery, 38(5), 3043–3101. https://doi.org/10.1007/s10618-022-00867-8
Troles, J., Schmid, U., Fan, W., & Tian, J. (2024). BAMFORESTS: Bamberg Benchmark Forest Dataset of Individual Tree Crowns in Very-High-Resolution UAV Images. Remote Sensing for Forest Morphological and Physiological Traits Monitoring, 16(11), 1–12. https://doi.org/10.3390/rs16111935
Wehner, C., Kertel, M., & Wewerka, J. (2024). Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs. arXiv. https://doi.org/10.48550/arxiv.2402.00043
Ai, L., Langer, J., Muggleton, S. H., & Schmid, U. (2023). Explanatory machine learning for sequential human teaching. Machine Learning, 112(10), 3591–3632. https://doi.org/10.1007/s10994-023-06351-8
Eirich, J. (2023). The Creation, Formalization, and Transfer of Expert Knowledge with Visual Analytics in Industrial Manufacturing Processes of Electrical Vehicles [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-58339
Eirich, J., Jackle, D., Sedlmair, M., Wehner, C., Schmid, U., Bernard, J., & Schreck, T. (2023). ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories. IEEE Transactions on Visualization and Computer Graphics, 29(8), 3441–3457. https://doi.org/10.1109/tvcg.2023.3279857
Finzel, B., Rieger, I., Kuhn, S., & Schmid, U. M. (2023). Domain-Specific Evaluation of Visual Explanations for Application-Grounded Facial Expression Recognition. Machine Learning and Knowledge Extraction: 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings, 31–44. https://doi.org/10.1007/978-3-031-40837-3_3
Heidrich, L., Slany, E., Scheele, S., & Schmid, U. (2023). FairCaipi: A Combination of Explanatory Interactive and Fair Machine Learning for Human and Machine Bias Reduction. Machine Learning and Knowledge Extraction, 5(4), 1519–1538. https://doi.org/10.3390/make5040076
Hirmer, T., Ochs, M., Stöckl, A., & Völker, A. (2023). Nutzung von Studienverlaufsdaten im Kontext eines Studienplanungsassistenten. Workshops der 21. Fachtagung Bildungstechnologien (DELFI), 177–180. https://doi.org/10.18420/wsdelfi2023-51
Holzinger, A., Saranti, A., Angerschmid, A., Finzel, B., Schmid, U., & Mueller, H. (2023). Toward human-level concept learning: Pattern benchmarking for AI algorithms. Patterns, 4(8). https://doi.org/10.1016/j.patter.2023.100788
Kiefer, S. (2023). Human-centered Interactions with Text Classifiers: Fusing Concept-based Knowledge with Local Surrogate Explanation Models [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-89771
Münsterberg, A. V., Budde, R., Schmid, U., & Leimbach, T. (2023). Perzeptrons programmieren und explorieren im Rahmen der Open Roberta Lernumgebung. Informatikunterricht zwischen Aktualität und Zeitlosigkeit: 20.-22.09.2023, Würzburg, Deutschland, 435–436. https://doi.org/10.18420/infos2023-056
Schramm, S. G., Wehner, C., & Schmid, U. (2023). Comprehensible Artificial Intelligence on Knowledge Graphs: A survey. Web Semantics, 79(December 2023, 100806), 1–17. https://doi.org/10.1016/j.websem.2023.100806
Troles, J., Nieding, R., Simons, S., & Schmid, U. (2023). Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data. Innovations for Community Services: 23rd International Conference, I4CS 2023, Bamberg, Germany, September 11–13, 2023; Proceedings, 103–122. https://doi.org/10.1007/978-3-031-40852-6_6
Wehner, C., Kertel, M., & Wewerka, J. (2023). Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs. 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring): Proceedings. 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring), 20-23 June 2023, Florence, Italy. https://doi.org/10.1109/vtc2023-spring57618.2023.10199563
Finzel, B., Saranti, A., Angerschmid, A., Tafler, D., Pfeifer, B., & Holzinger, A. (2022). Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs. Künstliche Intelligenz, 36(3–4), 271–285. https://doi.org/10.1007/s13218-022-00781-7
Göbel, K., Niessen, C., Seufert, S., & Schmid, U. (2022). Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations. Frontiers in Artificial Intelligence, 5(919534), 1–19. https://doi.org/10.3389/frai.2022.919534
Hepp, T., Zierk, J., Rauh, M., Metzler, M., & Seitz, S. (2022). Mixture density networks for the indirect estimation of reference intervals. BMC Bioinformatics, 23(1, 307), 1–17. https://doi.org/10.1186/s12859-022-04846-0
Kiefer, S. (2022). CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge. Information Fusion, 77, 184–195. https://doi.org/10.1016/j.inffus.2021.07.014
Kiefer, S., Hoffmann, M., & Schmid, U. (2022). Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions. Machine Learning and Knowledge Extraction, 4(4), 994–1010. https://doi.org/10.3390/make4040050
Mey, O., & Neufeld, D. (2022). Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation. Sensors, 22(23, 9037), 1–22. https://doi.org/10.3390/s22239037
Mohammed, A., Geppert, C., Hartmann, A., Kuritcyn, P., Bruns, V., Schmid, U., Wittenberg, T., Benz, M., & Finzel, B. (2022). Explaining and Evaluating Deep Tissue Classification by Visualizing Activations of Most Relevant Intermediate Layers. Current Directions in Biomedical Engineering, 8(2), 229–232. https://doi.org/10.1515/cdbme-2022-1059
Rieger, I., Pahl, J., Finzel, B., & Schmid, U. (2022). CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. 2022 26th International Conference on Pattern Recognition (ICPR): Proceedings, 798–804. https://doi.org/10.1109/ICPR56361.2022.9956319
Schmid, U. (2022). Constructing Explainability: Interdisciplinary Framework to Actively Shape Explanations in XAI. Künstliche Intelligenz, 36(3–4), 327–331. https://doi.org/10.1007/s13218-022-00767-5
Schmid, U., & Weitz, K. (2022). Künstliche Intelligenz und Psychologie - Von Kognitiver Modellierung bis Erklärbarkeit. In A. Schütz, M. Brand, & S. Steins-Löber (Eds.), Psychologie: eine Einführung in ihre Grundlagen und Anwendungsfelder (6., überarbeitete Auflage, pp. 219–231). Kohlhammer.
Schmid, U., & Wrede, B. (2022b). Explainable AI. Künstliche Intelligenz, 36(3–4), 207–210. https://doi.org/10.1007/s13218-022-00788-0
Schmid, U., & Wrede, B. (2022a). What is Missing in XAI So Far?: An Interdisciplinary Perspective. Künstliche Intelligenz, 36(3–4), 303–315. https://doi.org/10.1007/s13218-022-00786-2
Schwalbe, G. (2022). Concept Embedding Analysis Based Methods for the Safety Assurance of Deep Neural Networks: towards safe automotive computer vision applications [Otto-Friedrich-Universität]. https://doi.org/10.20378/irb-57172
Thaler, A. M., Mitrovic, A., & Schmid, U. (2022). Worked Examples as Application of Analogical Reasoning in Intelligent Tutoring and their Effects on SQL Competencies. KogWis 2022: Understanding Minds: 15th Biannual Conference of the German Society for Cognitive Science, 157–158. https://doi.org/10.6094/UNIFR/229611
Thaler, A. M., Paukner, F. K., Troles, J.-D., & Schmid, U. (2022). Individuelle Förderung von Programmierfertigkeiten im Studium am Beispiel von Intelligenten Tutor Systemen für SQL. In N. Vöing, S. Reisas, & M. Arnolds (Eds.), Scholarship of Teaching and Learning: Eine forschungsgeleitete Fundierung und Weiterentwicklung hochschul(fach)didaktischen Handelns (pp. 61–77). https://doi.org/10.57684/COS-986
Wehner, C., Powlesland, F., Altakrouri, B., & Schmid, U. (2022). Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation. Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence: 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, July 19–22, 2022, Proceedings, 13343, 621–632. https://doi.org/10.1007/978-3-031-08530-7_52
Deuschel, J., Finzel, B., & Rieger, I. (2021). Uncovering the Bias in Facial Expressions. Kolloquium Forschende Frauen 2020 - Gender in Gesellschaft 4.0: Beiträge Bamberger Nachwuchswissenschaftlerinnen, 15–42. https://doi.org/10.20378/irb-90482
Troles, J.-D., & Schmid, U. (2021). Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives. Sixth Conference on Machine Translation: Proceedings of the Conference, 531–541.
KI 2020: advances in artificial intelligence: 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020, proceedings. (2020). 43rd German Conference on AI, September 21–25, 2020, Bamberg, Germany. https://doi.org/10.1007/978-3-030-58285-2
Flener, P., & Schmid, U. M. (2020). Inductive Programming. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (pp. 658–666). Springer US. https://doi.org/10.1007/978-1-4899-7687-1_137
Wolking, M., & Schmid, U. (2017). Mental Models, Career Aspirations, and the Acquirement of Basic Concepts of Computer Science in Elementary Education. Proceedings of the 12th Workshop on Primary and Secondary Computing Education, 119–120. https://doi.org/10.1145/3137065.3137076
Approaches and Applications of Inductive Programming: third International Workshop, AAIP 2009, Edinburgh, UK, September 4, 2009; revised Papers. (2010). Third International Workshop, AAIP 2009, September 4, 2009, Edinburgh, UK. https://doi.org/10.1007/978-3-642-11931-6
Schmid, U., & Kaup, B. (1994). Der Einfluß von Beispielähnlichkeit auf induktive Lernprozesse beim rekursiven Programmieren. Technische Universität Berlin.