Bettina Finzel

Research Assistant (Doctoral Candidate)

Office: WE5/04.028
Consultation hour: by apppointment

Phone: +49 951 863 2878
Email: bettina.finzel(at)

Bettina Finzel holds a bachelor and a master degree in Applied Computer Science from the University of Bamberg. In 2018 she joined the Cognitive Systems Group as a research assistant and doctoral candidate in the project Transparent Medical Expert Companion (TraMeExCo), which has been funded by the Federal Ministry for Education and Research from September 2018 until December 2021. Since 2022 she continues her research on user-centric explainability for medical decision-making in the PainFaceReader Project, which is funded by the German Research Foundation. She is member of the German Society for Cognitive Science (GK) and of the Bamberg Graduate School of Affective and Cognitive Sciences (BaGrACS).

Short portrait of research at the Research in Bavaria webpage.

Profiles on Google Scholar, LinkedIn and researchgate.


Since 01/2022

Research Assistant in the project PainFaceReader founded by DFG at Cognitive Systems Group, Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany

10/2018 -


Research Assistant in the project Transparent Medical Expert Companion (TraMeExCo) founded by BMBF at Cognitive Systems Group, Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany
10/2016 - 10/2019Applied Computer Science Study at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany (Master of Science, with distinction)
04/2018 - 09/2018Internship concerning the Implementation of Predictive Analytics at MHP Management- und IT-Beratung GmbH
10/2012 - 03/2017Applied Computer Science Study at the Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg, Germany (Bachelor of Science)


Main Research Interests

  • User-centric explainability in medical decision-making, especially interpretable and interactive Machine Learning for human-AI partnerhips
  • Explainable Artificial Intelligence for Deep Learning-based classification
  • Neuro-Symbolic Computing


  • Inductive Logic Programming
  • Visual post-hoc explanations
  • Graph Neural Networks

Further Research Interests

  • Mentoring in Computer Science
  • Diversity and Fairness


  • Finzel B., Saranti A., Angerschmid A., Tafler D., Pfeifer S., 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 (to appear), 2022.
  • Finzel B., Kuhn S., Tafler D., Schmid U. (2022). Explaining with Attribute-based and Relational Near Misses: An Interpretable Approach to Distinguishing Facial Expressions of Pain and Disgust. 11th International Workshop on Approaches and Applications of Inductive Programming (AAIP, 2022, Cumberland Lodge, Great Windsor Park, UK) -- [Paper]
  • Rieger I., Pahl J., Finzel B., Schmid U. (2022). Regularization by Integrating Co-Occurrence Domain Knowledge for Affect Recognition. Workshop on Robust AI for High-Stakes Applications (RAI @ KI, 2022, virtual at Trier, Germany). -- [Abstract]
  • Schwalbe G., Finzel B. (2022). A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts. Data Mining and Knowledge Discovery (to appear), 2022. -- [Article]
  • Finzel B., Schmid U. (2022). Context-Aware XAI Methods for Joint Human-AI Problem Solving. Biannual Conference of the German Cognitive Science Society, (KogWis, 2022, Freiburg, Germany). -- [Abstract]
  • 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), 2022. -- [Paper]
  • Rieger I., Pahl J., Finzel B., Schmid U. (2022). CorrLoss: Integrating Co-Occurrence Domain Knowledge for Affect Recognition. International Conference on Pattern Recognition, (ICPR, 2022, Montreal, Canada). -- [Paper]


  • Finzel B., Tafler D., Thaler A., Schmid U. (2021). Multimodal Explanations for User-centric Medical Decision Support Systems. Association for the Advancement of Artificial Intelligence Fall 2021 Symposium in HUman partnership with Medical Artificial iNtelligence (AAAI-HUMAN.AI, virtual event, 4.-6.11.2021). -- [Paper]
  • Finzel B., Kollmann R., Rieger I., Pahl J., Schmid U. (2021). Deriving Temporal Prototypes from Saliency Map Clusters for the Analysis of Deep-Learning-based Facial Action Unit Classification. FG Knowledge Discovery und Machine Learning. LWDA 2021 at MCML, (LWDA, 01.09–03.09.2021., Munich, Germany). -- [Paper]
  • Finzel B., Tafler D., Scheele S., Schmid U. (2021). Explanation as a Process: User-centric Construction of Multi-level and Multi-modal Explanations. 44th German Conference on Artificial Intelligence, (KI, 27.9.-1.10.2021, Berlin, Germany). -- [Paper] - Best Paper nomination


  • Bruckert S., Finzel B., Schmid U. (2020). The next generation of medical decision support: a roadmap towards transparent expert companions. Frontiers in Artificial Intelligence, 3, p. 75 -- [Article]
  • Finzel B. (2020), Korrigierbares maschinelles Lernen mithilfe wechselseitiger Erklärungen am Beispiel der Medizin. Vol. 44, Medizininformatik. Gesellschaft für Informatik e.V.. (S. 14-17). -- [Article]
  • Deuschel J., Finzel B., Rieger I. (2020), Uncovering the Bias in Facial Expressions. FORSCHEnde FRAUEN Kolloquium, Bamberg University Press (zur Veröffentlichung angenommen). -- [Article]
  • Rieger I., Kollmann R., Finzel B., Seuß D., Schmid U. (2020), Verifying Deep Learning-based Decisions for Facial Expression Recognition. 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN, 22-24.4.2020, Bruges, Belgium). -- [Paper]
  • Schmid U., Finzel B. (2020), Mutual Explanations for Cooperative Decision Making in Medicine. Künstliche Intelligenz, 34(2), 227–233. -- [Article]


  • Rieger I., Finzel B., Seuß D., Wittenberg T., Schmid U. (2019) Make Pain Estimation Transparent: A Roadmap to Fuse Bayesian Deep Learning and Inductive Logic Programming (Poster). 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC, 23.-27. Juli 2019, Berlin). -- [Abstract]
  • Finzel B., Schmid U. (2019) Erklärbare KI für medizinische Anwendungen (Talk). 49. Kongress der Deutschen Gesellschaft für Endoskopie und Bildgebende Verfahren e.V. (28.-30. März 2019, Stuttgart), Track der Deutschen Gesellschaft für Biomedizinische Technik "Krankenhaus der Zukunft".
  • Finzel B., Rabold J., Schmid U. (2019) Explaining Relational Concepts: When Visualisation and Visual Interpretation of a Deep Neural Network's Decision are not enough (Abstract). Europäische Konferenz zur Datenanalyse (ECDA, 18.-20. März 2019, Bayreuth), Special Session on Interpretable Machine Learning. -- [Event]
  • Finzel, B., Sperker H.-C. (2019) Machine Learning goes E-Mobility: Mit Datenanalyse die Elektromobilität vorantreiben - Konzepte und Methoden zur Implementierung von Predictive-Analytics-Komponenten. JAXenter Online Magazin, 2019. -- [Article]


  • Finzel B., Deininger H., Schmid U. (2018) From beliefs to intention: mentoring as an approach to motivate female high school students to enrol in computer science studies. In Proceedings of the 4th Conference on Gender & IT (GenderIT '18). ACM, New York, NY, USA, 251-260.


  • Elmamooz, G., Finzel, B. & Nicklas, D., (2017). Towards Understanding Mobility in Museums. In: Mitschang, B., Nicklas, D., Leymann, F., Schöning, H., Herschel, M., Teubner, J., Härder, T., Kopp, O. & Wieland, M. (Hrsg.), Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband. Bonn: Gesellschaft für Informatik e.V.. (S. 127-136).

All Publications

Master's Thesis

  • Finzel B., (2019). Explanation-guided Constraint Generation for an Inverse Entailment Algorithm. Master's Thesis, University of Bamberg, 2019 (unpublished).

Bachelor's Thesis

  • Finzel B., (2017). A graph-based context model for PoI-based interactive guidance in museums. Bachelor's Thesis, University of Bamberg, 2017 (unpublished).


Interested students may contact me directly via email to request for supervision of a project or thesis topic.

Advised Student Theses

In progress:

  • Bianca Zimmer:  Explaining Decision Boundaries of CNNs Through Prototypes, Near Hits and Near Misses - A Comparison on Multiclass Medical Image Data Utilizing LRP Heatmaps (MA Survey Statistics, in progress)
  • Patrick Hilme: Verbally Explaining Image Classifications from Relevant Visual Features: Towards Learning Symbolic Structures Using Layer-wise Relevance Propagation and Logic Programming (MA AI, in progress)


  • Deepika Arneja:  Landmark-based Classification of Facial Expressions with a Spatio-Temporal Attention Graph Neural Network (MA SoSySc, finished in September 2022)
  • Simon Kuhn: Strategies for Privacy-preserving Classification of Facial Expressions (MA AI, finished in July 2022)
  • Nina Krob: Eine Multimodale Interaktive Schnittstelle zur Erklärung der Klassifikation Hierarchischer und Mehrwertiger Konzepte Basierend auf Logischer Programmierung (A Multimodal Interactive Interface to Explain the Classification of Hierarchical and Multiarity Concepts Based on Logic Programming) -- (MA AI, finished in March 2022)
  • Sonja Ruschhaupt: Explaining Image Classifications with Near Misses and Prototypes -- An Application to Facial Expression Analysis (BA AI, finished in November 2021)
  • Antonia Höfer: Enhancing the Interactive Machine Learning Companion LearnWithME with Global Constraints for Explanation-based Model Correction (BA AI, finished in October 2021)
  • Namrata Jain: Managing Chemical Production Processes with Deep (Transfer) Learning (MA ISSS, in collaboration with Wacker AG, finished in February 2021)
  • Rene Kollmann: Explaining Facial Expressions with Temporal Prototypes (MA AI, finished in December 2020)
  • Michael Fuchs:Towards Fast Interactive Machine Learning with Aleph (BA AI, finished in November 2020)
  • Isabel Saffer: Generierung und Evaluation von kontrastiven Erklärungen für die Paarweise Klassifikation von Partiell Geordneten Tumorklassen in der Histopathologie (Generating and Evaluating Contrastive Explanations for Pairwise Classification of Partially Ordered Tumorclasses in Histopathology) -- (BA AI, finished in August 2020)
  • Nina Krob: Ausdrucksstärke und Effizienz in der Generierung von Erklärungen: Vergleich von Association Rule Mining und Induktiver Logischer Programmierung für Interpretierbare Klassifikation von Schmerz (Expressiveness and Efficiency in Explanation Generation: Comparing Association Rule Mining and Inductive Logic Programming for Interpretable Pain Classification) (BA AI, finished in July 2020)
  • Simon Kuhn: Identifying Near Misses for Relational Concepts with Graph Matching – Explaining Classifier Decisions of Facial Expressions (BA AI, finished in June 2019)

Advised Student Projects

Summer Term 2021

  • 15 ECTS Project (Simon Kuhn): Automatic Verification of Convolutional Neural Networks: A Landmark-based Approach for Facial Action Unit Classification

Summer Term 2022

  • 15 ECTS Project (David Tafler): Classifying Relational Visual Concepts in Images with a Neuro-Symbolic Approach

Advised Student Seminars



  • Gedanken zu Künstlicher Intelligenz und der künftigen Gesellschaft, Diskussionspanel Gesellschaft 4.0, Jahreskonferenz des Deutsch-Tschechischen Gesprächsforums zum Thema "Die Zukunft der deutsch-tschechischen Beziehungen aus der Sicht junger Deutscher und Tschechen", Olomouc, 20.11.2018 -- [Program]
  • Künstliche Intelligenz und Medizin: Von operierenden Robotern und Computerdiagnosen, Tag der Begabtenförderung 2019, Kloster Banz, 8.7.2019 -- [Report]
  • Learning Expressive First Order Rules - Introduction to Inductive Logic Programming, talk held in KIZI seminar, University of Economics Prague, 22.10.2019 -- [Event]
  • Skalpell bitte! KI in der Medizin: Chancen und Herausforderungen, KI und Wir* Convention, Magdeburg, 23.11.2019 -- [Program], [Abstract], [News]


  • Constraints as Verbal Corrective Feedback -- An Inductive Logic Programming Approach to Generate and Adapt Explanations in Image Classification, 1st GK Doctoral Symposium on Cognitive Science, 23.-24.01.2020 -- [Abstract]
  • Constraints as Verbal Corrective Feedback -- An Inductive Logic Programming Approach to Generate and Adapt Explanations in Image Classification, Virtuelle Konferenz der GI Fachgruppe Frauen und Informatik, 25.04.2020 -- [Program]
  • Uncovering the Bias in Facial Expressions, FORSCHEnde FRAUEN Kolloquium der Universität Bamberg, 23.-30.06.2020 -- [Program], [News], [Article], [Video]


  • A hybrid explanation framework for decision-making in medicine, ADA Day Fraunhofer IIS, 25.05.2021. -- [Website of Institute]
  • Towards a hybrid explanation framework, Fraunhofer ITEM at group of Prof. Dr. Lena Wiese, 02.06.2021. -- [Website of Institute]
  • Künstliche Intelligenz - Konkurrenz oder Partnerschaft, Frauennetzwerk Fürth (virtual event), 08.06.2021 -- [Event]

Workshops and Lectures

  • Workshops
    • Das Mentoring Program make IT, 16. Arbeitstagung der Konferenz der Einrichtungen für Frauen- und Geschlechterstudien im deutschsprachigen Raum (KEG), Arbeitsgruppe „Mentoringprojekte in der Informatik“, University of Music and Performing Arts, Vienna, 26.9.2018 -- [Program]
    • Shiny R - Analysen interaktiv gestalten und visualisieren, course at the TAO-SFZ-Workshop „KI selber programmieren“, University of Bamberg 29.-31.10.2018 -- [Report]
    • Maschinelles Lernen und Data Science – Hands-On mit KNIME, Woche der Forschung, University of Bamberg, 26.2.2019 und 23.10.2020 -- [Program]
  • Lectures
    • Classifying Medical Data with Neural Nets and Inductive Logic Programming, lecture including practical tasks in R and Prolog, University of Economics Prague, 22.10.2019 -- [Event]
    • Learning from Mutual Explanations for Cooperative Decision Making in Medicine - Ute Schmid and Bettina Finzel at the AI4Health Lecture Series, University of Luxembourg, 04.11.2020 -- [Abstract]
    • Two modules on Interactive Machine Learning and Explaining Deep-learning classifier with LIME for the online learning course Explainable Machine Learning for Engineering, AI-Campus, funded by the German Federal Ministry of Education and Research (BMBF) -- [Website of the platform]

Participation in Schools and Seminars

  • Interdisciplinary College 2019 Günne/Möhnesee Germany “Out of your senses: from data to insight”, intense one-week spring school, 12.-18.3.2019
  • ECML PKDD Summer School 2019 Würzburg Germany “Machine Learning and Data Mining for Geo-Spatial Data/Volunteered Geographic Information, Quality of Experience and Human-Computer Interaction (EPSS19)”, intense one-week spring school, 11.-16.10.2019, included paper preparation in group work for submission at an international conference
  • Interdisciplinary College 2021 Günne/Möhnesee Germany (virtual event) “Connected in Cyberspace”, intense four-week spring school, 12.3.-9.4.2021
  • Invited participant at Dagstuhl Seminar on Approaches and Applications of Inductive Programming 2021, joint work with Ute Schmid on Ultra-strong machine learning with explanatory dialogs -- [Report]


  • STRL at IJCAI 2022
  • KogWis 2022
  • Information Processing and Management Journal 2022
  • XAI4debugging at NeurIPS 2021 (outstanding reviewer team)
  • iMIMIC at MICCAI 2020, 2021, 2022
  • ECML/PKDD Conference 2020, 2021 (subreviewer)
  • KI Conference 2019, 2022 (subreviewer)
  • ACM SIGMIS Database Journal 2021
  • ACM SIGMIS Conference 2020 (subreviewer)
  • ACM GEWINN Conference 2018
  • IET Book Review 2021 (subreviewer)

Memberships and Voluntary Services


  • Finalist of the competition "KI-Newcomer*innen Wettbewerb 2019" of the German Informatics Society, -- [News]
  • Finalist of the Future X Healthcare Scientific Excellence Award 2019, Roche, Munich -- [News]
  • Female Tech Talents Stipendium (2018) awarded by Campusjäger
  • Award of the Ministry for Environment and Health (2012) for a Sustainability and Nutrition Education Project for Elementary Schools