Schulz, C. (n.d.). Feeds. Ein zentrales Strukturprinzip sozialer Medien. In R. Dörre & A. Tuschling (Eds.), Handbuch Social Media: Geschichte – Kultur – Ästhetik (1st ed.). Metzler Verlag.

Lammert, O., Richter, B., Schütze, C., Thommes, K., & Wrede, B. (2024). Humans in XAI: Increased Reliance in Decision-Making Under Uncertainty by Using Explanation Strategies. Frontiers in Behavioral Economics. https://doi.org/10.3389/frbhe.2024.1377075

Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388–14396. https://doi.org/10.1609/aaai.v38i13.29352

Banh, N. C., & Scharlau, I. (2024). Effects of task difficulty on visual processing speed. Tagung experimentell arbeitender Psycholog:innen (TeaP), Regensburg.

Stratmann, L., Banh, N. C., Scharlau, I., & Dressler, F. (2024). Safety Assistance Systems for Bicyclists: Toward Empirical Studies of the Dooring Problem. ACM Symposium on Principles of Distributed Computing (PODC 2024), Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for Distributed Systems (ApPLIED 2024). https://doi.org/10.1145/3663338.3665831

Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190

Schulz, C. (2024). Vernakulärer Code oder die Geister, die der Algorithmus rief - digitale Schriftlichkeit im Kontext von sozialen Medienplattformen. In M. Bartelmus & A. Nebrig (Eds.), Digitale Schriftlichkeit – Progammieren, Prozessieren und Codieren von Schrift (1st ed.). transcript . https://doi.org/10.1515/9783839468135-009

Schulz, C. (n.d.). On “Super Likes” and Algorithmic (In)Visibilities: Frictions Between Social and Economic Logics in the Context of Social Media Platforms. Digital Culture & Society , 2.

Robrecht, A., Voss, H., Gottschalk, L., & Kopp, S. (2024). Integrating Representational Gestures into Automatically Generated  Embodied Explanations and its Effects on Understanding and Interaction  Quality. In arXiv:2406.12544.

Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3520–3528). PMLR.

Thommes, K., Lammert, O., Schütze, C., Richter, B., & Wrede, B. (2024). Human Emotions in AI Explanations. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-63803-9_15

Beierling, H., & Vollmer, A.-L. (2024). The Power of Combined Modalities in Interactive Robot Learning. In arXiv:2405.07817.

Beierling, H., Loos, K., Helmert, R., & Vollmer, A.-L. (2024). Advancing Human-Robot Collaboration: The Impact of Flexible Input Mechanisms. Robotics: Science and Systems, Delf.

Sengupta, M., El Baff, R., Alshomary, M., & Wachsmuth, H. (2024). Analyzing the Use of Metaphors in News Editorials for Political Framing. In K. Duh, H. Gomez, & S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 3621–3631). Association for Computational Linguistics.

Alpsancar, S. (2024). Warum und wozu erklärbare KI? Über die Verschiedenheit dreier paradigmatischer Zwecksetzungen. In R. Adolphi, S. Alpsancar, S. Hahn, & M. Kettner (Eds.), Philosophische Digitalisierungsforschung  Verantwortung, Verständigung, Vernunft, Macht (pp. 55–113). transcript.

Wang, Y., Xu, Y., Skantze, G., & Buschmeier, H. (2024). How much does nonverbal communication conform to entropy rate constancy?: A case study on listener gaze in interaction. Findings of the Association for Computational Linguistics ACL 2024, 3533–3545.

Battefeld, D., Mues, S., Wehner, T., House, P., Kellinghaus, C., Wellmer, J., & Kopp, S. (2024). Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive Process Trajectories. Proceedings of the 46th Annual Conference of the Cognitive Science Society. The Annual Meeting of the Cognitive Science Society, Rotterdam, NL.

Wagner, P., Włodarczak, M., Buschmeier, H., Türk, O., & Gilmartin, E. (2024). Turn-taking dynamics across different phases of explanatory dialogues. Proceedings of the 28th Workshop on the Semantics and Pragmatics of Dialogue, 6–14.

Buhl, H. M., Fischer , J. B., & Rohlfing, K. (2024). Changes in partner models – Effects of adaptivity in the course of explanations. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.

Pilán, I., Prévot, L., Buschmeier, H., & Lison, P. (2024). Conversational feedback in scripted versus spontaneous dialogues: A comparative analysis. Proceedings of the 25th Meeting of the Special Interest Group on Discourse and Dialogue, 440–457. https://doi.org/10.18653/v1/2024.sigdial-1.38

Booshehri, M., Buschmeier, H., Cimiano, P., Kopp, S., Kornowicz, J., Lammert, O., Matarese, M., Mindlin, D., Robrecht, A. S., Vollmer, A.-L., Wagner, P., & Wrede, B. (2024). Towards a Computational Architecture for Co-Constructive Explainable Systems. Proceedings of the 2024 Workshop on Explainability Engineering, 20–25. https://doi.org/10.1145/3648505.3648509

Riechmann, A. N., & Buschmeier, H. (2024). Automatic reconstruction of dialogue participants’ coordinating gaze behavior from multiple camera perspectives. Book of Abstracts of the 2nd International Multimodal Communication Symposium, 38–39.

Liedeker, F., Sanchez-Graillet, O., Seidler, M., Brandt, C., Wellmer, J., & Cimiano, P. (n.d.). A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support. First Workshop on Natural Language Argument-Based Explanations, Santiago de Compostela, Spain.

Liedeker, F., Düsing, C., Nieveler, M., & Cimiano, P. (2024). An Empirical Investigation of Users’ Assessment of XAI Explanations: Identifying the Sweet-Spot of Explanation Complexity. 2nd World Conference on eXplainable Artificial Intelligence, Valetta, Malta.

Battefeld, D., Liedeker, F., Cimiano, P., & Kopp, S. (2024). ASCODI: An XAI-based interactive reasoning support system for justifiable medical diagnosing. Proceedings of the 1st Workshop on Multimodal, Affective and Interactive EXplainable AI (MAI-XAI). European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain.

Singh, A., & Rohlfing, K. J. (2024). Coupling of Task and Partner Model: Investigating the Intra-Individual Variability in Gaze during Human–Robot Explanatory Dialogue. Proceedings of 26th ACM International Conference on Multimodal Interaction (ICMI 2024). 26th ACM International Conference on Multimodal Interaction (ICMI 2024), San Jose, Costa Rica. https://doi.org/10.1145/3686215.3689202

Klowait, N., Erofeeva, M., Lenke, M., Horwath, I., & Buschmeier, H. (2024). Can AI explain AI? Interactive co-construction of explanations among human and artificial agents. Discourse & Communication, 18(6), 917–930. https://doi.org/10.1177/17504813241267069

Booshehri, M., Buschmeier, H., & Cimiano, P. (2024). A model of factors contributing to the success of dialogical explanations. Proceedings of the 26th ACM International Conference on Multimodal Interaction, 373–381. https://doi.org/10.1145/3678957.3685744

Türk, O., Lazarov, S., Wang, Y., Buschmeier, H., Grimminger, A., & Wagner, P. (2024). Predictability of understanding in explanatory interactions based on multimodal cues. Proceedings of the 26th ACM International Conference on Multimodal Interaction, 449–458. https://doi.org/10.1145/3678957.3685741

Buschmeier, H., Kopp, S., & Hassan, T. (2024). Multimodal Co-Construction of Explanations with XAI Workshop. Proceedings of the 26th ACM International Conference on Multimodal Interaction, 698–699. https://doi.org/10.1145/3678957.3689205

Kornowicz, J., & Thommes, K. (2024). Algorithm, Expert, or Both? Evaluating the Role of Feature Selection Methods on User Preferences and Reliance. ArXiv. https://doi.org/10.48550/ARXIV.2408.01171

Schütze, C., Richter, B., Lammert, O., Thommes, K., & Wrede, B. (2024). Static Socio-demographic and Individual Factors for Generating Explanations in XAI: Can they serve as a prior in DSS for adaptation of explanation strategies? HAI ’24: Proceedings of the 12th International Conference on Human-Agent Interaction, 141–149. https://doi.org/10.1145/3687272.3688300

Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190

Thommes, K., Lammert, O., Schütze, C., Richter, B., & Wrede, B. (2024). Human Emotions in AI Explanations.

Booshehri, M., Buschmeier, H., & Cimiano, P. (2024). Towards a BFO-based ontology of understanding in explanatory interactions. Proceedings of the 4th International Workshop on Data Meets Applied Ontologies in Explainable AI (DAO-XAI). 4th International Workshop on Data Meets Applied Ontologies in Explainable AI (DAO-XAI), Santiago de Compostela, Spain.

Lazarov, S. T., & Grimminger, A. (2024). Variations in explainers’ gesture deixis in explanations related to the monitoring of explainees’ understanding. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.

Schaffer, M. E., Terfloth, L., Schulte, C., & Buhl, H. M. (2024). Perception and Consideration of the Explainees’ Needs for Satisfying Explanations. 2nd World Conference on eXplainable Artificial Intelligence, Valletta, Malta.

Schaffer, M. E., Terfloth, L., Schulte, C., & Buhl, H. M. (2024). Explainers’ Mental Representations of Explainees’ Needs in Everyday Explanations. Joint Proceedings of the XAI-2024 Late-Breaking Work, Demos and Doctoral Consortium. 3793.

Schulz, C. (n.d.). Vom foto-sozialen Graph zum Story-Format: Über die Institutionalisierung sozialmedialer Infrastruktur aus dem Geiste der Fotografie. In A. Schürmann & K. Yacavone (Eds.), Die Fotografie und ihre Institutionen. Von der Lehrsammlung zum Bundesinstitut (1st ed.). Reimer Verlag. https://doi.org/doi.org/10.5771/9783496030980

Kornowicz, J. (2024). An Empirical Examination of the Evaluative AI Framework. ArXiv. https://doi.org/10.48550/ARXIV.2411.08583

Banh, N. C., Tünnermann, J., Rohlfing, K. J., & Scharlau, I. (2024). Benefiting from Binary Negations? Verbal Negations Decrease Visual Attention and Balance Its Distribution. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1451309

Heid, S., Kornowicz, J., Hanselle, J. M., Hüllermeier, E., & Thommes, K. (2024). Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems. PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 21, 233.

Alshomary, M., Lange, F., Booshehri, M., Sengupta, M., Cimiano, P., & Wachsmuth, H. (2024). Modeling the Quality of Dialogical Explanations. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 11523–11536). ELRA and ICCL.

Reijers, W., Matzner, T., Alpsancar, S., & Philippi, M. (2024). AI explainability, temporality, and civic virtue. Smart Ethics in the Digital World: Proceedings of the ETHICOMP 2024. 21th International Conference on the Ethical and Social Impacts of ICT. Universidad de La Rioja, 2024.

Alpsancar, S., Buhl, H. M., Matzner, T., & Scharlau, I. (2024). Explanation needs and ethical demands: unpacking the instrumental value of XAI. AI and Ethics. https://doi.org/10.1007/s43681-024-00622-3

Alpsancar, S., Matzner, T., & Philippi, M. (2024). Unpacking the purposes of explainable AI. Smart Ethics in the Digital World: Proceedings of the ETHICOMP 2024. 21th International Conference on the Ethical and Social Impacts of ICT, 31–35.

Rautenberg, F., Kuhlmann, M., Wiechmann, J., Seebauer, F., Wagner, P., & Haeb-Umbach, R. (2023). On Feature Importance and Interpretability of Speaker Representations. ITG Conference on Speech Communication. ITG Conference on Speech Communication, Aachen.

Wiechmann, J., Rautenberg, F., Wagner, P., & Haeb-Umbach, R. (2023). Explaining voice characteristics to novice voice practitioners-How successful is it? 20th International Congress of the Phonetic Sciences (ICPhS) .

Lebedeva, A., Kornowicz, J., Lammert, O., & Papenkordt, J. (2023). The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks. Artificial Intelligence in HCI. https://doi.org/10.1007/978-3-031-35891-3_9

Peters, T. M., & Visser, R. W. (2023). The Importance of Distrust in AI. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-44070-0_15

Seebauer, F., Kuhlmann, M., Haeb-Umbach, R., & Wagner, P. (2023). Re-examining the quality dimensions of synthetic speech. 12th Speech Synthesis Workshop (SSW) 2023.

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning. https://doi.org/10.1007/s10994-023-06385-y

Artelt, A., Visser, R., & Hammer, B. (2023). “I do not know! but why?” — Local model-agnostic example-based explanations of reject. Neurocomputing, 558, Article 126722. https://doi.org/10.1016/j.neucom.2023.126722

Reijers, W. (2023). Technology and Civic Virtue. Philosophy & Technology, 36(4), Article 71. https://doi.org/10.1007/s13347-023-00669-w

Kornowicz, J., & Thommes, K. (2023). Aggregating Human Domain Knowledge for Feature Ranking. Artificial Intelligence in HCI. https://doi.org/10.1007/978-3-031-35891-3_7

Groß, A., Schütze, C., Brandt, M., Wrede, B., & Richter, B. (2023). RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1245501

Scharlau, I., & Karsten, A. (2023). Schreibfokussierte Graduiertenförderung: Reflexive Spezialisierung für interdisziplinäre Forschungskontexte. In B. Berendt, A. Fleischmann, G. Salmhofer, N. Schaper, B. Szczyrba, M. Wiemer, & J. Wildt (Eds.), Neues Handbuch Hochschullehre (Vol. 111, pp. 17–35). DUZ medienhaus.

Terfloth, L., Schaffer, M., Buhl, H. M., & Schulte, C. (2023). Adding Why to What? Analyses of an Everyday Explanation. 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), Lisboa. https://doi.org/10.1007/978-3-031-44070-0_13

Alpsancar, S. (2023). What is AI Ethics? Ethics as means of self-regulation and the need for critical reflection . International Conference on Computer Ethics 2023, 1(1), 1--17.

Schulz, C. (2023). From mental models to algorithmic imaginaries to co-constructive mental models. Navigationen – Zeitschrift Für Medien- Und Kulturwissenschaften , 2, 65–75. http://dx.doi.org/10.25819/ubsi/10428

Schulz, C., & Schröter, J. (2023). Tech/Imaginations – Introduction. Navigationen – Zeitschrift Für Medien- Und Kulturwissenschaften , 2, 7–14. http://dx.doi.org/10.25819/ubsi/10428

Schulz, C. (2023). A new algorithmic imaginary. Media, Culture & Society, 45(3), 646–655. https://doi.org/10.1177/01634437221136014

Johnson, D., Hakobyan, O., & Drimalla, H. (2023). Towards Interpretability in Audio and Visual Affective Machine Learning: A Review.

Richter, B., Putze, F., Ivucic, G., Brandt, M., Schütze, C., Reisenhofer, R., Wrede, B., & Schultz, T. (2023). EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study. Multimodal Technologies and Interaction, 7(4), Article 37. https://doi.org/10.3390/mti7040037

Robrecht, A., & Kopp, S. (2023). SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation. Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 48–58. https://doi.org/10.5220/0011671300003393

Dyck, L., Beierling, H., Helmert, R., & Vollmer, A.-L. (2023). Technical Transparency for Robot Navigation Through AR Visualizations. Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 720–724. https://doi.org/10.1145/3568294.3580181

Esposito, E. (2023). Does Explainability Require Transparency? Sociologica, 16(3), 17–27. https://doi.org/10.6092/ISSN.1971-8853/15804

Esposito, E. (2023). Explaining Machines: Social Management of Incomprehensible Algorithms. Introduction. Sociologica, 16(3), 1–4. https://doi.org/10.6092/ISSN.1971-8853/16265

Rautenberg, F., Kuhlmann, M., Ebbers, J., Wiechmann, J., Seebauer, F., Wagner, P., & Haeb-Umbach, R. (2023). Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics. Fortschritte Der Akustik - DAGA 2023, 1409–1412.

Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2023). iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. In Machine Learning and Knowledge Discovery in Databases: Research Track. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-43418-1_26

Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In Communications in Computer and Information Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44064-9_11

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning, 112(12), 4863–4903. https://doi.org/10.1007/s10994-023-06385-y

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 Proceedings. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-148

Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS 2023 - Advances in Neural Information Processing Systems, 36, 11515--11551.

Sengupta, M., Alshomary, M., Scharlau, I., & Wachsmuth, H. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. Findings of the Association for Computational Linguistics: EMNLP 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.308

Sengupta, M., Alshomary, M., & Wachsmuth, H. (2023). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. Proceedings of the 3rd Workshop on Figurative Language Processing (FLP). https://doi.org/10.18653/v1/2022.flp-1.19

Klowait, N. (2023). On the Multimodal Resolution of a Search Sequence in Virtual Reality. Human Behavior and Emerging Technologies, 2023, 1–15. https://doi.org/10.1155/2023/8417012

Klowait, N., & Erofeeva, M. (2023). Halting the Decay of Talk. Social Interaction. Video-Based Studies of Human Sociality, 6(1). https://doi.org/10.7146/si.v6i1.136903

Banh, N. C., & Scharlau, I. (2023). First steps towards real-time assessment of attentional weights and capacity according to TVA. In S. Merz, C. Frings, B. Leuchtenberg, B. Moeller, S. Mueller, R. Neumann, B. Pastötter, L. Pingen, & G. Schui (Eds.), Abstracts of the 65th TeaP. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.12945

Groß, A., Singh, A., Banh, N. C., Richter, B., Scharlau, I., Rohlfing, K. J., & Wrede, B. (2023). Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1236184

Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic Scoring Lists for Interpretable Machine Learning. In Discovery Science. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45275-8_13

Tech/Imaginations. (2023). In C. Schulz, J. Schröter, & C. Ernst (Eds.), Navigationen – Zeitschrift für Medien- und Kulturwissenschaften (Vol. 2). Universi Verlag . http://dx.doi.org/10.25819/ubsi/10428

Schütze, C., Lammert, O., Richter, B., Thommes, K., & Wrede, B. (2023). Emotional Debiasing Explanations for Decisions in HCI. Artificial Intelligence in HCI. https://doi.org/10.1007/978-3-031-35891-3_20

Robrecht, A., & Kopp, S. (2023). SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation. Proceedings of the 15th International Conference on Agents and Artificial Intelligence. https://doi.org/10.5220/0011671300003393

Robrecht, A., Rothgänger, M., & Kopp, S. (2023). A Study on the Benefits and Drawbacks of Adaptivity in AI-generated Explanations. Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents. https://doi.org/10.1145/3570945.3607339

Fisher, J., Robrecht, A., Kopp, S., & Rohlfing, K. (2023). Exploring the Semantic Dialogue Patterns of Explanations – a Case Study of Game Explanations. Proceedings of the 27th Workshop on the Semantics and Pragmatics of Dialogue . Semdial, Maribor.

Buschmeier, H., Buhl, H. M., Kern, F., Grimminger, A., Beierling, H., Fisher, J., Groß, A., Horwath, I., Klowait, N., Lazarov, S., Lenke, M., Lohmer, V., Rohlfing, K., Scharlau, I., Singh, A., Terfloth, L., Vollmer, A.-L., Wang, Y., Wilmes, A., & Wrede, B. (2023). Forms of Understanding of XAI-Explanations. In arXiv:2311.08760.

Beierling, H., Richter, P., Brandt, M., Terfloth, L., Schulte, C., Wersing, H., & Vollmer, A.-L. (2023). What you need to know about a learning robot: Identifying the enabling  architecture of complex systems. In arXiv:2311.14431.

Sengupta, M., Alshomary, M., Scharlau, I., & Wachsmuth, H. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 4636–4659). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.308

Beer, F., & Schulz, C. (n.d.). The Return of Black Box Theory in Explainable AI. 4S Conference (Society for the Social Studies of Science), Honolulu/Hawaii, November 9.

Schulz, C., & Wilmes , A. (n.d.). Vernacular Metaphors of AI .

Karsten, A. (2023). Voices in Dialogue: Taking Polyphony in Academic Writing Seriously. Written Communication, 41(1), 6–36. https://doi.org/10.1177/07410883231207104

Wang, Y., & Buschmeier, H. (2023). Does listener gaze in face-to-face interaction follow the Entropy Rate Constancy principle: An empirical study. Findings of the Association for Computational Linguistics: EMNLP 2023, 15372–15379.

Singh, A., & Rohlfing, K. J. (2023). Contrastiveness in the context of action demonstration: an eye-tracking study on its effects on action perception and action recall. Proceedings of the Annual Meeting of the Cognitive Science Society 45 (45). 45th Annual Conference of the Cognitive Science Society, Sydney.

Liedeker, F., & Cimiano, P. (2023). A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations. xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lissabon.

Liedeker, F., & Cimiano, P. (2023). Dynamic Feature Selection in AI-based Diagnostic Decision Support for Epilepsy. 1st International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Breckenridge, CO, USA .

Buschmeier, H., Buhl, H. M., Kern, F., Grimminger, A., Beierling, H., Fisher, J., Groß, A., Horwath, I., Klowait, N., Lazarov, S., Lenke, M., Lohmer, V., Rohlfing, K., Scharlau, I., Singh, A., Terfloth, L., Vollmer, A.-L., Wang, Y., Wilmes, A., & Wrede, B. (2023). Forms of Understanding of XAI-Explanations. In arXiv:2311.08760.

Lazarov, S. T., Schaffer, M., & Ronoh, E. K. (2023). Approaches of Assessing Understanding Using Video-Recall Data. 2nd TRR 318 Conference “Measuring Understanding” , Paderborn.

Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., & Hüllermeier, E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In M. Leyer & J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. .

Wiechmann, J., Glarner, T., Rautenberg, F., Wagner, P., & Haeb-Umbach, R. (2022). Technically enabled explaining of voice characteristics. 18. Phonetik Und Phonologie Im Deutschsprachigen Raum (P&P).

Lammert, O., Richter, B., Schütze, C., Thommes, K., & Wrede, B. (2024). Humans in XAI: Increased Reliance in Decision-Making Under Uncertainty by Using Explanation Strategies. Frontiers in Behavioral Economics. https://doi.org/10.3389/frbhe.2024.1377075

Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388–14396. https://doi.org/10.1609/aaai.v38i13.29352

Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190

Schulz, C. (n.d.). On “Super Likes” and Algorithmic (In)Visibilities: Frictions Between Social and Economic Logics in the Context of Social Media Platforms. Digital Culture & Society , 2.

Buhl, H. M., Fischer , J. B., & Rohlfing, K. (2024). Changes in partner models – Effects of adaptivity in the course of explanations. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.

Klowait, N., Erofeeva, M., Lenke, M., Horwath, I., & Buschmeier, H. (2024). Can AI explain AI? Interactive co-construction of explanations among human and artificial agents. Discourse & Communication, 18(6), 917–930. https://doi.org/10.1177/17504813241267069

Kornowicz, J., & Thommes, K. (2024). Algorithm, Expert, or Both? Evaluating the Role of Feature Selection Methods on User Preferences and Reliance. ArXiv. https://doi.org/10.48550/ARXIV.2408.01171

Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogues for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171, Article 109190. https://doi.org/10.1016/j.ijar.2024.109190

Kornowicz, J. (2024). An Empirical Examination of the Evaluative AI Framework. ArXiv. https://doi.org/10.48550/ARXIV.2411.08583

Banh, N. C., Tünnermann, J., Rohlfing, K. J., & Scharlau, I. (2024). Benefiting from Binary Negations? Verbal Negations Decrease Visual Attention and Balance Its Distribution. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1451309

Alpsancar, S., Buhl, H. M., Matzner, T., & Scharlau, I. (2024). Explanation needs and ethical demands: unpacking the instrumental value of XAI. AI and Ethics. https://doi.org/10.1007/s43681-024-00622-3

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning. https://doi.org/10.1007/s10994-023-06385-y

Artelt, A., Visser, R., & Hammer, B. (2023). “I do not know! but why?” — Local model-agnostic example-based explanations of reject. Neurocomputing, 558, Article 126722. https://doi.org/10.1016/j.neucom.2023.126722

Reijers, W. (2023). Technology and Civic Virtue. Philosophy & Technology, 36(4), Article 71. https://doi.org/10.1007/s13347-023-00669-w

Kornowicz, J., & Thommes, K. (2023). Aggregating Human Domain Knowledge for Feature Ranking. Artificial Intelligence in HCI. https://doi.org/10.1007/978-3-031-35891-3_7

Groß, A., Schütze, C., Brandt, M., Wrede, B., & Richter, B. (2023). RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1245501

Schulz, C. (2023). From mental models to algorithmic imaginaries to co-constructive mental models. Navigationen – Zeitschrift Für Medien- Und Kulturwissenschaften , 2, 65–75. http://dx.doi.org/10.25819/ubsi/10428

Schulz, C., & Schröter, J. (2023). Tech/Imaginations – Introduction. Navigationen – Zeitschrift Für Medien- Und Kulturwissenschaften , 2, 7–14. http://dx.doi.org/10.25819/ubsi/10428

Schulz, C. (2023). A new algorithmic imaginary. Media, Culture & Society, 45(3), 646–655. https://doi.org/10.1177/01634437221136014

Richter, B., Putze, F., Ivucic, G., Brandt, M., Schütze, C., Reisenhofer, R., Wrede, B., & Schultz, T. (2023). EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study. Multimodal Technologies and Interaction, 7(4), Article 37. https://doi.org/10.3390/mti7040037

Esposito, E. (2023). Does Explainability Require Transparency? Sociologica, 16(3), 17–27. https://doi.org/10.6092/ISSN.1971-8853/15804

Esposito, E. (2023). Explaining Machines: Social Management of Incomprehensible Algorithms. Introduction. Sociologica, 16(3), 1–4. https://doi.org/10.6092/ISSN.1971-8853/16265

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). Incremental permutation feature importance (iPFI): towards online explanations on data streams. Machine Learning, 112(12), 4863–4903. https://doi.org/10.1007/s10994-023-06385-y

Klowait, N. (2023). On the Multimodal Resolution of a Search Sequence in Virtual Reality. Human Behavior and Emerging Technologies, 2023, 1–15. https://doi.org/10.1155/2023/8417012

Klowait, N., & Erofeeva, M. (2023). Halting the Decay of Talk. Social Interaction. Video-Based Studies of Human Sociality, 6(1). https://doi.org/10.7146/si.v6i1.136903

Groß, A., Singh, A., Banh, N. C., Richter, B., Scharlau, I., Rohlfing, K. J., & Wrede, B. (2023). Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue. Frontiers in Robotics and AI, 10. https://doi.org/10.3389/frobt.2023.1236184

Karsten, A. (2023). Voices in Dialogue: Taking Polyphony in Academic Writing Seriously. Written Communication, 41(1), 6–36. https://doi.org/10.1177/07410883231207104

Schmid, U., & Wrede, B. (2022). What is Missing in XAI So Far? KI - Künstliche Intelligenz, 36(3–4), 303–315. https://doi.org/10.1007/s13218-022-00786-2

Schmid, U., & Wrede, B. (2022). Explainable AI. KI - Künstliche Intelligenz, 36(3–4), 207–210. https://doi.org/10.1007/s13218-022-00788-0

Wrede, B. (2022). AI: Back to the Roots? KI - Künstliche Intelligenz, 36(2), 117–120. https://doi.org/10.1007/s13218-022-00773-7

Fisher, J. B., Lohmer, V., Kern, F., Barthlen, W., Gaus, S., & Rohlfing, K. (2022). Exploring Monological and Dialogical Phases in Naturally Occurring Explanations. KI - Künstliche Intelligenz, 36(3–4), 317–326. https://doi.org/10.1007/s13218-022-00787-1

Rohlfing, K., Vollmer, A.-L., Fritsch, J., & Wrede, B. (2022). Which “motionese” parameters change with children’s age? Disentangling attention-getting from action-structuring modifications. Frontiers in Communication, 7. https://doi.org/10.3389/fcomm.2022.922405

Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2022). Agnostic Explanation of Model Change based on Feature Importance. KI - Künstliche Intelligenz, 36(3–4), 211–224. https://doi.org/10.1007/s13218-022-00766-6

Axelsson, A., Buschmeier, H., & Skantze, G. (2022). Modeling Feedback in Interaction With Conversational Agents—A Review. Frontiers in Computer Science, 4. https://doi.org/10.3389/fcomp.2022.744574

Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier, H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier, E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth, H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366

Stratmann, L., Banh, N. C., Scharlau, I., & Dressler, F. (2024). Safety Assistance Systems for Bicyclists: Toward Empirical Studies of the Dooring Problem. ACM Symposium on Principles of Distributed Computing (PODC 2024), Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for Distributed Systems (ApPLIED 2024). https://doi.org/10.1145/3663338.3665831

Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. In S. Dasgupta, S. Mandt, & Y. Li (Eds.), Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3520–3528). PMLR.

Thommes, K., Lammert, O., Schütze, C., Richter, B., & Wrede, B. (2024). Human Emotions in AI Explanations. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-63803-9_15

Beierling, H., Loos, K., Helmert, R., & Vollmer, A.-L. (2024). Advancing Human-Robot Collaboration: The Impact of Flexible Input Mechanisms. Robotics: Science and Systems, Delf.

Sengupta, M., El Baff, R., Alshomary, M., & Wachsmuth, H. (2024). Analyzing the Use of Metaphors in News Editorials for Political Framing. In K. Duh, H. Gomez, & S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 3621–3631). Association for Computational Linguistics.

Wang, Y., Xu, Y., Skantze, G., & Buschmeier, H. (2024). How much does nonverbal communication conform to entropy rate constancy?: A case study on listener gaze in interaction. Findings of the Association for Computational Linguistics ACL 2024, 3533–3545.

Battefeld, D., Mues, S., Wehner, T., House, P., Kellinghaus, C., Wellmer, J., & Kopp, S. (2024). Revealing the Dynamics of Medical Diagnostic Reasoning as Step-by-Step Cognitive Process Trajectories. Proceedings of the 46th Annual Conference of the Cognitive Science Society. The Annual Meeting of the Cognitive Science Society, Rotterdam, NL.

Wagner, P., Włodarczak, M., Buschmeier, H., Türk, O., & Gilmartin, E. (2024). Turn-taking dynamics across different phases of explanatory dialogues. Proceedings of the 28th Workshop on the Semantics and Pragmatics of Dialogue, 6–14.

Pilán, I., Prévot, L., Buschmeier, H., & Lison, P. (2024). Conversational feedback in scripted versus spontaneous dialogues: A comparative analysis. Proceedings of the 25th Meeting of the Special Interest Group on Discourse and Dialogue, 440–457. https://doi.org/10.18653/v1/2024.sigdial-1.38

Booshehri, M., Buschmeier, H., Cimiano, P., Kopp, S., Kornowicz, J., Lammert, O., Matarese, M., Mindlin, D., Robrecht, A. S., Vollmer, A.-L., Wagner, P., & Wrede, B. (2024). Towards a Computational Architecture for Co-Constructive Explainable Systems. Proceedings of the 2024 Workshop on Explainability Engineering, 20–25. https://doi.org/10.1145/3648505.3648509

Riechmann, A. N., & Buschmeier, H. (2024). Automatic reconstruction of dialogue participants’ coordinating gaze behavior from multiple camera perspectives. Book of Abstracts of the 2nd International Multimodal Communication Symposium, 38–39.

Liedeker, F., Sanchez-Graillet, O., Seidler, M., Brandt, C., Wellmer, J., & Cimiano, P. (n.d.). A User Study Evaluating Argumentative Explanations in Diagnostic Decision Support. First Workshop on Natural Language Argument-Based Explanations, Santiago de Compostela, Spain.

Liedeker, F., Düsing, C., Nieveler, M., & Cimiano, P. (2024). An Empirical Investigation of Users’ Assessment of XAI Explanations: Identifying the Sweet-Spot of Explanation Complexity. 2nd World Conference on eXplainable Artificial Intelligence, Valetta, Malta.

Battefeld, D., Liedeker, F., Cimiano, P., & Kopp, S. (2024). ASCODI: An XAI-based interactive reasoning support system for justifiable medical diagnosing. Proceedings of the 1st Workshop on Multimodal, Affective and Interactive EXplainable AI (MAI-XAI). European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain.

Singh, A., & Rohlfing, K. J. (2024). Coupling of Task and Partner Model: Investigating the Intra-Individual Variability in Gaze during Human–Robot Explanatory Dialogue. Proceedings of 26th ACM International Conference on Multimodal Interaction (ICMI 2024). 26th ACM International Conference on Multimodal Interaction (ICMI 2024), San Jose, Costa Rica. https://doi.org/10.1145/3686215.3689202

Booshehri, M., Buschmeier, H., & Cimiano, P. (2024). A model of factors contributing to the success of dialogical explanations. Proceedings of the 26th ACM International Conference on Multimodal Interaction, 373–381. https://doi.org/10.1145/3678957.3685744

Türk, O., Lazarov, S., Wang, Y., Buschmeier, H., Grimminger, A., & Wagner, P. (2024). Predictability of understanding in explanatory interactions based on multimodal cues. Proceedings of the 26th ACM International Conference on Multimodal Interaction, 449–458. https://doi.org/10.1145/3678957.3685741

Schütze, C., Richter, B., Lammert, O., Thommes, K., & Wrede, B. (2024). Static Socio-demographic and Individual Factors for Generating Explanations in XAI: Can they serve as a prior in DSS for adaptation of explanation strategies? HAI ’24: Proceedings of the 12th International Conference on Human-Agent Interaction, 141–149. https://doi.org/10.1145/3687272.3688300

Thommes, K., Lammert, O., Schütze, C., Richter, B., & Wrede, B. (2024). Human Emotions in AI Explanations.

Booshehri, M., Buschmeier, H., & Cimiano, P. (2024). Towards a BFO-based ontology of understanding in explanatory interactions. Proceedings of the 4th International Workshop on Data Meets Applied Ontologies in Explainable AI (DAO-XAI). 4th International Workshop on Data Meets Applied Ontologies in Explainable AI (DAO-XAI), Santiago de Compostela, Spain.

Lazarov, S. T., & Grimminger, A. (2024). Variations in explainers’ gesture deixis in explanations related to the monitoring of explainees’ understanding. Proceedings of the Annual Meeting of the Cognitive Science Society, 46.

Schaffer, M. E., Terfloth, L., Schulte, C., & Buhl, H. M. (2024). Perception and Consideration of the Explainees’ Needs for Satisfying Explanations. 2nd World Conference on eXplainable Artificial Intelligence, Valletta, Malta.

Schaffer, M. E., Terfloth, L., Schulte, C., & Buhl, H. M. (2024). Explainers’ Mental Representations of Explainees’ Needs in Everyday Explanations. Joint Proceedings of the XAI-2024 Late-Breaking Work, Demos and Doctoral Consortium. 3793.

Heid, S., Kornowicz, J., Hanselle, J. M., Hüllermeier, E., & Thommes, K. (2024). Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems. PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 21, 233.

Alshomary, M., Lange, F., Booshehri, M., Sengupta, M., Cimiano, P., & Wachsmuth, H. (2024). Modeling the Quality of Dialogical Explanations. In N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, & N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (pp. 11523–11536). ELRA and ICCL.

Rautenberg, F., Kuhlmann, M., Wiechmann, J., Seebauer, F., Wagner, P., & Haeb-Umbach, R. (2023). On Feature Importance and Interpretability of Speaker Representations. ITG Conference on Speech Communication. ITG Conference on Speech Communication, Aachen.

Wiechmann, J., Rautenberg, F., Wagner, P., & Haeb-Umbach, R. (2023). Explaining voice characteristics to novice voice practitioners-How successful is it? 20th International Congress of the Phonetic Sciences (ICPhS) .

Lebedeva, A., Kornowicz, J., Lammert, O., & Papenkordt, J. (2023). The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks. Artificial Intelligence in HCI. https://doi.org/10.1007/978-3-031-35891-3_9

Peters, T. M., & Visser, R. W. (2023). The Importance of Distrust in AI. Communications in Computer and Information Science. https://doi.org/10.1007/978-3-031-44070-0_15

Seebauer, F., Kuhlmann, M., Haeb-Umbach, R., & Wagner, P. (2023). Re-examining the quality dimensions of synthetic speech. 12th Speech Synthesis Workshop (SSW) 2023.

Terfloth, L., Schaffer, M., Buhl, H. M., & Schulte, C. (2023). Adding Why to What? Analyses of an Everyday Explanation. 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), Lisboa. https://doi.org/10.1007/978-3-031-44070-0_13

Alpsancar, S. (2023). What is AI Ethics? Ethics as means of self-regulation and the need for critical reflection . International Conference on Computer Ethics 2023, 1(1), 1--17.

Robrecht, A., & Kopp, S. (2023). SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation. Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 48–58. https://doi.org/10.5220/0011671300003393

Dyck, L., Beierling, H., Helmert, R., & Vollmer, A.-L. (2023). Technical Transparency for Robot Navigation Through AR Visualizations. Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 720–724. https://doi.org/10.1145/3568294.3580181

Rautenberg, F., Kuhlmann, M., Ebbers, J., Wiechmann, J., Seebauer, F., Wagner, P., & Haeb-Umbach, R. (2023). Speech Disentanglement for Analysis and Modification of Acoustic and Perceptual Speaker Characteristics. Fortschritte Der Akustik - DAGA 2023, 1409–1412.

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 Proceedings. ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online. https://doi.org/10.14428/esann/2023.es2023-148

Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2023). SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS 2023 - Advances in Neural Information Processing Systems, 36, 11515--11551.

Sengupta, M., Alshomary, M., Scharlau, I., & Wachsmuth, H. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. Findings of the Association for Computational Linguistics: EMNLP 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.308

Sengupta, M., Alshomary, M., & Wachsmuth, H. (2023). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. Proceedings of the 3rd Workshop on Figurative Language Processing (FLP). https://doi.org/10.18653/v1/2022.flp-1.19

Schütze, C., Lammert, O., Richter, B., Thommes, K., & Wrede, B. (2023). Emotional Debiasing Explanations for Decisions in HCI. Artificial Intelligence in HCI. https://doi.org/10.1007/978-3-031-35891-3_20

Robrecht, A., & Kopp, S. (2023). SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation. Proceedings of the 15th International Conference on Agents and Artificial Intelligence. https://doi.org/10.5220/0011671300003393

Robrecht, A., Rothgänger, M., & Kopp, S. (2023). A Study on the Benefits and Drawbacks of Adaptivity in AI-generated Explanations. Proceedings of the 23rd ACM International Conference on Intelligent Virtual Agents. https://doi.org/10.1145/3570945.3607339

Fisher, J., Robrecht, A., Kopp, S., & Rohlfing, K. (2023). Exploring the Semantic Dialogue Patterns of Explanations – a Case Study of Game Explanations. Proceedings of the 27th Workshop on the Semantics and Pragmatics of Dialogue . Semdial, Maribor.

Sengupta, M., Alshomary, M., Scharlau, I., & Wachsmuth, H. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 4636–4659). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.308

Beer, F., & Schulz, C. (n.d.). The Return of Black Box Theory in Explainable AI. 4S Conference (Society for the Social Studies of Science), Honolulu/Hawaii, November 9.

Schulz, C., & Wilmes , A. (n.d.). Vernacular Metaphors of AI .

Wang, Y., & Buschmeier, H. (2023). Does listener gaze in face-to-face interaction follow the Entropy Rate Constancy principle: An empirical study. Findings of the Association for Computational Linguistics: EMNLP 2023, 15372–15379.

Singh, A., & Rohlfing, K. J. (2023). Contrastiveness in the context of action demonstration: an eye-tracking study on its effects on action perception and action recall. Proceedings of the Annual Meeting of the Cognitive Science Society 45 (45). 45th Annual Conference of the Cognitive Science Society, Sydney.

Liedeker, F., & Cimiano, P. (2023). A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations. xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lissabon.

Hanselle, J. M., Kornowicz, J., Heid, S., Thommes, K., & Hüllermeier, E. (2023). Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. In M. Leyer & J. Wichmann (Eds.), LWDA’23: Learning, Knowledge, Data, Analysis. .

Wiechmann, J., Glarner, T., Rautenberg, F., Wagner, P., & Haeb-Umbach, R. (2022). Technically enabled explaining of voice characteristics. 18. Phonetik Und Phonologie Im Deutschsprachigen Raum (P&P).

Groß, A., Schütze, C., Wrede, B., & Richter, B. (2022). An Architecture Supporting Configurable Autonomous Multimodal Joint-Attention-Therapy for Various Robotic Systems. INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, 154–159. https://doi.org/10.1145/3536220.3558070

Schütze, C., Groß, A., Wrede, B., & Richter, B. (2022). Enabling Non-Technical Domain Experts to Create Robot-Assisted Therapeutic Scenarios via Visual Programming. INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, 166–170. https://doi.org/10.1145/3536220.3558072

Sieger, L. N., Hermann, J., Schomäcker, A., Heindorf, S., Meske, C., Hey, C.-C., & Doğangün, A. (2022). User Involvement in Training Smart Home Agents. International Conference on Human-Agent Interaction. HAI ’22: International Conference on Human-Agent Interaction, Christchurch, New Zealand. https://doi.org/10.1145/3527188.3561914

Finke, J., Horwath, I., Matzner, T., & Schulz, C. (2022). (De)Coding social practice in the field of XAI: Towards a co-constructive framework of explanations and understanding between lay users and algorithmic systems. Artificial Intelligence in HCI, 149–160. https://doi.org/10.1007/978-3-031-05643-7_10

Wachsmuth, H., & Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. In N. Calzolari, C.-R. Huang, H. Kim, J. Pustejovsky, L. Wanner, K.-S. Choi, P.-M. Ryu, H.-H. Chen, L. Donatelli, H. Ji, S. Kurohashi, P. Paggio, N. Xue, S. Kim, Y. Hahm, Z. He, T. K. Lee, E. Santus, F. Bond, & S.-H. Na (Eds.), Proceedings of the 29th International Conference on Computational Linguistics (pp. 344–354). International Committee on Computational Linguistics.

Sengupta, M., Alshomary, M., & Wachsmuth, H. (2022). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. Proceedings of the 2022 Workshop on Figurative Language Processing.

Finke, J., Horwath, I., Matzner, T., & Schulz, C. (2022). (De)Coding Social Practice in the Field of XAI: Towards a Co-constructive Framework of Explanations and Understanding Between Lay Users and Algorithmic Systems. Artificial Intelligence in HCI, 149–160. https://doi.org/10.1007/978-3-031-05643-7_10

Battefeld, D., & Kopp, S. (2022). Formalizing cognitive biases in medical diagnostic reasoning. Proceedings of the 8th Workshop on Formal and Cognitive Reasoning. 8th Workshop on Formal and Cognitive Reasoning (FCR), Trier.

Banh, N. C., & Scharlau, I. (2024). Effects of task difficulty on visual processing speed. Tagung experimentell arbeitender Psycholog:innen (TeaP), Regensburg.

Buschmeier, H., Kopp, S., & Hassan, T. (2024). Multimodal Co-Construction of Explanations with XAI Workshop. Proceedings of the 26th ACM International Conference on Multimodal Interaction, 698–699. https://doi.org/10.1145/3678957.3689205

Reijers, W., Matzner, T., Alpsancar, S., & Philippi, M. (2024). AI explainability, temporality, and civic virtue. Smart Ethics in the Digital World: Proceedings of the ETHICOMP 2024. 21th International Conference on the Ethical and Social Impacts of ICT. Universidad de La Rioja, 2024.

Alpsancar, S., Matzner, T., & Philippi, M. (2024). Unpacking the purposes of explainable AI. Smart Ethics in the Digital World: Proceedings of the ETHICOMP 2024. 21th International Conference on the Ethical and Social Impacts of ICT, 31–35.

Banh, N. C., & Scharlau, I. (2023). First steps towards real-time assessment of attentional weights and capacity according to TVA. In S. Merz, C. Frings, B. Leuchtenberg, B. Moeller, S. Mueller, R. Neumann, B. Pastötter, L. Pingen, & G. Schui (Eds.), Abstracts of the 65th TeaP. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.12945

Liedeker, F., & Cimiano, P. (2023). Dynamic Feature Selection in AI-based Diagnostic Decision Support for Epilepsy. 1st International Conference on Artificial Intelligence in Epilepsy and Neurological Disorders, Breckenridge, CO, USA .

Lazarov, S. T., Schaffer, M., & Ronoh, E. K. (2023). Approaches of Assessing Understanding Using Video-Recall Data. 2nd TRR 318 Conference “Measuring Understanding” , Paderborn.

Banh, N. C., & Scharlau, I. (2022). Effects of verbal negation on TVA’s capacity and weight parameters. In S. Malejka, M. Barth, H. Haider, & C. Stahl (Eds.), TeaP 2022 - Abstracts of the 64th Conference of Experimental Psychologists . Pabst Science Publishers. https://doi.org/10.23668/psycharchives.5677

Banh, N. C., Scharlau, I., & Rohlfing, K. J. (2022). Folgen wiederholter Negation auf die Aufmerksamkeit. In C. Bermeitinger & W. Greve (Eds.), 52. Kongress der Deutschen Gesellschaft für Psychologie.