Publikationen

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

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.

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. Special Issue 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. (2022b). 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

Wehner, C., Powlesland, F., Altakrouri, B., & Schmid, U. (2022a). 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, 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.