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Wang, Y., Türk, O., Grimminger, A., & Buschmeier, H. (2026). Predicting states of understanding in explanatory interactions using cognitive load-related linguistic cues. Proceedings of the 15th Language Resources and Evaluation Conference, 11368–11378. https://doi.org/10.63317/4tsmsshhd3ad

Wang, Y., Lao, L., Huang, L., Skantze, G., Xu, Y., & Buschmeier, H. (2026). Investigating the representation of backchannels and fillers in fine-tuned language models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics, 5319–5348. https://doi.org/10.18653/v1/2026.acl-long.241

Türk, O., Lazarov, S. T., Wang, Y., Grimminger, A., Buschmeier, H., & Wagner, P. (n.d.). When “yeah” means “not quite”: Multimodal detection of backchannels expressing incomplete understanding. Proceedings of INTERSPEECH 2026. INTERSPEECH 2026, Sydney, Australia.

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Mahmood, Y., Hecher, M., & Ngonga Ngomo, A.-C. (2025). Dung’s Argumentation Framework: Unveiling the Expressive Power with Inconsistent Databases. Proceedings of the AAAI Conference on Artificial Intelligence, 39(14), 15058–15066. https://doi.org/10.1609/aaai.v39i14.33651

Muschalik, M., Fumagalli, F., Frazzetto, P., Strotherm, J., Hermes, L., Sperduti, A., Hüllermeier, E., & Hammer, B. (2025). Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks. The Thirteenth International Conference on Learning Representations (ICLR).

Visser, R., Fumagalli, F., Hüllermeier, E., & Hammer, B. (2025). Explaining Outliers using Isolation Forest and Shapley Interactions. Proceedings of the European Symposium on Artificial Neural Networks (ESANN).

Fumagalli, F., Muschalik, M., Hüllermeier, E., Hammer, B., & Herbinger, J. (2025). Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 258, 5140–5148.

Fichtel, L., Spliethöver, M., Hüllermeier, E., Jimenez, P., Klowait, N., Kopp, S., Ngonga Ngomo, A.-C., Robrecht, A., Scharlau, I., Terfloth, L., Vollmer, A.-L., & Wachsmuth, H. (n.d.). Investigating Co-Constructive Behavior of Large Language Models in  Explanation Dialogues. Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Annual Meeting of the Special Interest Group on Discourse and Dialogue.

Spliethöver, M., Knebler, T., Fumagalli, F., Muschalik, M., Hammer, B., Hüllermeier, E., & Wachsmuth, H. (2025). Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 2421–2449). Association for Computational Linguistics.

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Battefeld, D., & Kopp, S. (2025). Implementing a computational cognitive process model of medical diagnostic reasoning. Proceedings of KogWis 2025: Conference of the German Cognitive Science Society. Flexible Minds: Situated and Comparative Perspectives (KogWis 2025), Bochum, Germany.

Singh, A., & Rohlfing, K. J. (2025). Manners Matter: Action history guides attention and repair choices during interaction. IEEE International Conference on Development and Learning (ICDL). IEEE International Conference on Development and Learning (ICDL), Prague. https://doi.org/10.31234/osf.io/yn2we_v1

Singh, A., & Rohlfing, K. J. (2025). Embedding Psycholinguistics: An Interactive Framework for Studying Language in Action. 6th Biannual Conference of the German Society for Cognitive Science, Bochum, Germany. 6th Biannual Conference of the German Society for Cognitive Science, Bochum, Germany, Bochum. https://doi.org/10.17605/OSF.IO/8PR23

Rautenberg, F., Seebauer, F., Wiechmann, J., Kuhlmann, M., Wagner, P., & Haeb-Umbach, R. (2025). Synthesizing Speech with Selected Perceptual Voice Qualities – A Case Study with Creaky Voice. Interspeech 2025. Interspeech, Rotterdam. https://doi.org/10.21437/Interspeech.2025-1443

Sengupta, M., Muschalik, M., Fumagalli, F., Hammer, B., Hüllermeier, E., Ghosh, D., & Wachsmuth, H. (2025). Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions. Accepted in Findings . Empirical Methods in Natural Language Processing (EMNLP 2025).

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

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.

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). 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.

Xu, Y., Wang, Y., An, H., Liu, Z., & Li, Y. (2024). Detecting subtle differences between human and model languages using spectrum of relative likelihood. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 10108–10121. https://doi.org/10.18653/v1/2024.emnlp-main.564

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

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

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.

Kenneweg, P., Kenneweg, T., Fumagalli, F., & Hammer, B. (2024). No learning rates needed: Introducing SALSA - Stable Armijo Line Search Adaptation. 2024 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN60899.2024.10650124

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 (AAAI), 38(13), 14388–14396. https://doi.org/10.1609/aaai.v38i13.29352

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

Fumagalli, F., Muschalik, M., Kolpaczki, P., Hüllermeier, E., & Hammer, B. (2024). KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. Proceedings of the 41st International Conference on Machine Learning (ICML), 235, 14308–14342.

Muschalik, M., Baniecki, H., Fumagalli, F., Kolpaczki, P., Hammer, B., & Huellermeier, E. (2024). shapiq: Shapley interactions for machine learning. Advances in Neural Information Processing Systems (NeurIPS), 37, 130324–130357.

Kolpaczki, P., Bengs, V., Muschalik, M., & Hüllermeier, E. (2024). Approximating the shapley value without marginal contributions. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 38(12), 13246–13255.

Wang, Y., & Buschmeier, H. (2024). Revisiting the phenomenon of syntactic complexity convergence on German dialogue data. Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024), 75–80.

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.

Spliethöver, M., Menon, S. N., & Wachsmuth, H. (2024). Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Findings of the Association for Computational Linguistics: ACL 2024 (pp. 9294–9313). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.553

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.

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.

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.

Buschmeier, H., Cimiano, P., Kopp, S., Kornowicz, J., Lammert, O., Matarese, M., Mindlin, D., Robrecht, A. S., Vollmer, A.-L., Wagner, P., Wrede, B., & Booshehri, M. (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

Türk, O., Lazarov, S. T., 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

Mahmood, Y., Hecher, M., & Ngonga Ngomo, A.-C. (2024). Dung’s Argumentation Framework: Unveiling the Expressive Power with  Inconsistent Databases. https://doi.org/10.1609/AAAI.V39I14.33651

Sieker, J., Junker, S., Utescher, R., Attari, N., Wersing, H., Buschmeier, H., & Zarrieß, S. (2024). The illusion of competence: Evaluating the effect of explanations on users’ mental models of visual question answering systems. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 19459–19475. https://doi.org/10.18653/v1/2024.emnlp-main.1084

Hecher, M., Mahmood, Y., Meier, A., & Schmidt, J. (2024). Quantitative Claim-Centric Reasoning in Logic-Based Argumentation. Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/377

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.

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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.

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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.

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

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.

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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.

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

Fisher, J. B., Robrecht, A., Kopp, S., & Rohlfing, K. J. (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.

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.

Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., & Huellermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. Proceedings of the World Conference on Explainable Artificial Intelligence (XAI). https://doi.org/10.1007/978-3-031-44064-9_11

Fumagalli, F., Muschalik, M., Hüllermeier, E., & Hammer, B. (2023). On Feature Removal for Explainability in Dynamic Environments. Proceedings of the European Symposium on Artificial Neural Networks (ESANN). 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. Advances in Neural Information Processing Systems (NeurIPS), 36, 11515--11551.

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. .

Alshomary, M., & Wachsmuth, H. (2023). Conclusion-based Counter-Argument Generation. In A. Vlachos & I. Augenstein (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 957–967). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.67

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

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.

Artelt, A., Brinkrolf, J., Visser, R., & Hammer, B. (2022). Explaining Reject Options of Learning Vector Quantization Classifiers. Proceedings of the 14th International Joint Conference on Computational Intelligence. https://doi.org/10.5220/0011389600003332

Artelt, A., Visser, R., & Hammer, B. (2022). Model Agnostic Local Explanations of Reject. ESANN 2022 Proceedings. https://doi.org/10.14428/esann/2022.es2022-34

Alshomary, M., Rieskamp, J., & Wachsmuth, H. (2022). Generating Contrastive Snippets for Argument Search. Proceedings of the 9th International Conference on Computational Models of Argument, 21–31. http://dx.doi.org/10.3233/FAIA220138

Alshomary, M., El Baff, R., Gurcke, T., & Wachsmuth, H. (2022). The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 8782–8797.

Porwol, P. F., Körber, M., Kern, F., Schulte, C., & Scharlau, I. (2026). Framing what and how to think: Lay people’s metaphors for algorithms. In P. Cimiano, B. Paaßen, & A.-L. Vollmer (Eds.), Proceedings of the 3rd TRR 318 Conference: Contextualizing Explanations. Bielefeld University Press. https://doi.org/10.64136/ubio9074

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Fisher, J. B., & Terfloth, L. (2025). The Dual Nature as a Local Context to Explore Verbal Behaviour in Game Explanations. Proceedings of the 29th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2025).

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