@inproceedings{51367,
  author       = {{Robrecht, Amelie and Kopp, Stefan}},
  booktitle    = {{Proceedings of the 15th International Conference on Agents and Artificial Intelligence}},
  isbn         = {{978-989-758-623-1}},
  location     = {{Lisbon}},
  pages        = {{48--58}},
  publisher    = {{SCITEPRESS - Science and Technology Publications}},
  title        = {{{SNAPE: A Sequential Non-Stationary Decision Process Model for Adaptive Explanation Generation}}},
  doi          = {{10.5220/0011671300003393}},
  year         = {{2023}},
}

@inproceedings{55156,
  author       = {{Fisher, Josephine Beryl and Robrecht, Amelie and Kopp, Stefan and Rohlfing, Katharina J.}},
  booktitle    = {{Proceedings of the 27th Workshop on the Semantics and Pragmatics of Dialogue }},
  location     = {{Maribor}},
  title        = {{{Exploring the Semantic Dialogue Patterns of Explanations – a Case Study of Game Explanations}}},
  year         = {{2023}},
}

@article{50262,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.</jats:p>}},
  author       = {{Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara}},
  issn         = {{0885-6125}},
  journal      = {{Machine Learning}},
  keywords     = {{Artificial Intelligence, Software}},
  number       = {{12}},
  pages        = {{4863--4903}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Incremental permutation feature importance (iPFI): towards online explanations on data streams}}},
  doi          = {{10.1007/s10994-023-06385-y}},
  volume       = {{112}},
  year         = {{2023}},
}

@inproceedings{55909,
  abstract     = {{It is generally assumed that language (written and spoken) follows the entropy rate constancy (ERC) principle, which states that the information density of a text is constant over time. Recently, this has also been found for nonverbal gestures used in monologue, but it is still unclear whether the ERC principle also applies to listeners' nonverbal signals. We focus on listeners' gaze behaviour extracted from video-recorded conversations and trained a transformer-based neural sequence model to process the gaze data of the dialogues and compute its information density. We also compute the information density of the corresponding speech using a pre-trained language model. Our results show (1) that listeners' gaze behaviour in dialogues roughly follows the ERC principle, as well as (2) a congruence between information density of speech and listeners' gaze behaviour.}},
  author       = {{Wang, Yu and Buschmeier, Hendrik}},
  booktitle    = {{Findings of the Association for Computational Linguistics: EMNLP 2023}},
  location     = {{Singapore}},
  pages        = {{15372–15379}},
  title        = {{{Does listener gaze in face-to-face interaction follow the Entropy Rate Constancy principle: An empirical study}}},
  year         = {{2023}},
}

@inproceedings{48778,
  author       = {{Muschalik, Maximilian and Fumagalli, Fabian and Jagtani, Rohit and Hammer, Barbara and Huellermeier, Eyke}},
  booktitle    = {{Proceedings of the World Conference on Explainable Artificial Intelligence (xAI)}},
  isbn         = {{9783031440632}},
  issn         = {{9783031440649}},
  title        = {{{iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios}}},
  doi          = {{10.1007/978-3-031-44064-9_11}},
  year         = {{2023}},
}

@inbook{48776,
  author       = {{Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}},
  booktitle    = {{Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference (ECML PKDD)}},
  isbn         = {{9783031434174}},
  issn         = {{1611-3349}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams}}},
  doi          = {{10.1007/978-3-031-43418-1_26}},
  year         = {{2023}},
}

@inproceedings{48775,
  author       = {{Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara}},
  booktitle    = {{Proceedings of the European Symposium on Artificial Neural Networks (ESANN)}},
  location     = {{Bruges (Belgium) and online}},
  title        = {{{On Feature Removal for Explainability in Dynamic Environments}}},
  doi          = {{10.14428/ESANN/2023.ES2023-148}},
  year         = {{2023}},
}

@inproceedings{52230,
  author       = {{Fumagalli, Fabian and Muschalik, Maximilian and Kolpaczki, Patrick and Hüllermeier, Eyke and Hammer, Barbara}},
  booktitle    = {{Advances in Neural Information Processing Systems (NeurIPS)}},
  pages        = {{11515----11551}},
  title        = {{{SHAP-IQ: Unified Approximation of any-order Shapley Interactions}}},
  volume       = {{36}},
  year         = {{2023}},
}

@article{61301,
  author       = {{Artelt, André and Visser, Roelof and Hammer, Barbara}},
  issn         = {{0925-2312}},
  journal      = {{Neurocomputing}},
  publisher    = {{Elsevier BV}},
  title        = {{{“I do not know! but why?” — Local model-agnostic example-based explanations of reject}}},
  doi          = {{10.1016/j.neucom.2023.126722}},
  volume       = {{558}},
  year         = {{2023}},
}

@inproceedings{58723,
  abstract     = {{In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument‘s conclusion and to ensure that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.}},
  author       = {{Alshomary, Milad and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics}},
  editor       = {{Vlachos, Andreas and Augenstein, Isabelle}},
  pages        = {{957–967}},
  publisher    = {{Association for Computational Linguistics}},
  title        = {{{Conclusion-based Counter-Argument Generation}}},
  doi          = {{10.18653/v1/2023.eacl-main.67}},
  year         = {{2023}},
}

@inproceedings{47448,
  abstract     = {{In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.}},
  author       = {{Terfloth, Lutz and Schaffer, Michael and Buhl, Heike M. and Schulte, Carsten}},
  isbn         = {{978-3-031-44069-4}},
  location     = {{Lisboa}},
  publisher    = {{Springer, Cham}},
  title        = {{{Adding Why to What? Analyses of an Everyday Explanation}}},
  doi          = {{10.1007/978-3-031-44070-0_13}},
  year         = {{2023}},
}

@inproceedings{33696,
  author       = {{Wiechmann, Jana and Glarner, Thomas and Rautenberg, Frederik and Wagner, Petra and Haeb-Umbach, Reinhold}},
  booktitle    = {{18. Phonetik und Phonologie im deutschsprachigen Raum (P&P)}},
  location     = {{Bielefeld}},
  title        = {{{Technically enabled explaining of voice characteristics}}},
  year         = {{2022}},
}

@article{51348,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>With the perspective on applications of AI-technology, especially data intensive deep learning approaches, the need for methods to control and understand such models has been recognized and gave rise to a new research domain labeled explainable artificial intelligence (XAI). In this overview paper we give an interim appraisal of what has been achieved so far and where there are still gaps in the research. We take an interdisciplinary perspective to identify challenges on XAI research and point to open questions with respect to the quality of the explanations regarding faithfulness and consistency of explanations. On the other hand we see a need regarding the interaction between XAI and user to allow for adaptability to specific information needs and explanatory dialog for informed decision making as well as the possibility to correct models and explanations by interaction. This endeavor requires an integrated interdisciplinary perspective and rigorous approaches to empirical evaluation based on psychological, linguistic and even sociological theories.</jats:p>}},
  author       = {{Schmid, Ute and Wrede, Britta}},
  issn         = {{0933-1875}},
  journal      = {{KI - Künstliche Intelligenz}},
  keywords     = {{Artificial Intelligence}},
  number       = {{3-4}},
  pages        = {{303--315}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{What is Missing in XAI So Far?}}},
  doi          = {{10.1007/s13218-022-00786-2}},
  volume       = {{36}},
  year         = {{2022}},
}

@article{51366,
  author       = {{Schmid, Ute and Wrede, Britta}},
  issn         = {{0933-1875}},
  journal      = {{KI - Künstliche Intelligenz}},
  keywords     = {{Artificial Intelligence}},
  number       = {{3-4}},
  pages        = {{207--210}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Explainable AI}}},
  doi          = {{10.1007/s13218-022-00788-0}},
  volume       = {{36}},
  year         = {{2022}},
}

@article{51365,
  author       = {{Wrede, Britta}},
  issn         = {{0933-1875}},
  journal      = {{KI - Künstliche Intelligenz}},
  keywords     = {{Artificial Intelligence}},
  number       = {{2}},
  pages        = {{117--120}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{AI: Back to the Roots?}}},
  doi          = {{10.1007/s13218-022-00773-7}},
  volume       = {{36}},
  year         = {{2022}},
}

@article{51344,
  abstract     = {{<jats:p>Modified action demonstration—dubbed <jats:italic>motionese—</jats:italic>has been proposed as a way to help children recognize the structure and meaning of actions. However, until now, it has been investigated only in young infants. This brief research report presents findings from a cross-sectional study of parental action demonstrations to three groups of 8–11, 12–23, and 24–30-month-old children that applied seven motionese parameters; a second study investigated the youngest group of participants longitudinally to corroborate the cross-sectional results. Results of both studies suggested that four motionese parameters (Motion Pauses, Pace, Velocity, Acceleration) seem to structure the action by organizing it in motion pauses. Whereas these parameters persist over different ages, three other parameters (Demonstration Length, Roundness, and Range) occur predominantly in the younger group and seem to serve to organize infants' attention on the basis of movement. Results are discussed in terms of facilitative vs. pedagogical learning.</jats:p>}},
  author       = {{Rohlfing, Katharina and Vollmer, Anna-Lisa and Fritsch, Jannik and Wrede, Britta}},
  issn         = {{2297-900X}},
  journal      = {{Frontiers in Communication}},
  keywords     = {{Social Sciences (miscellaneous), Communication}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Which “motionese” parameters change with children's age? Disentangling attention-getting from action-structuring modifications}}},
  doi          = {{10.3389/fcomm.2022.922405}},
  volume       = {{7}},
  year         = {{2022}},
}

@inproceedings{51346,
  author       = {{Groß, André and Schütze, Christian and Wrede, Britta and Richter, Birte}},
  booktitle    = {{INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION}},
  pages        = {{154--159}},
  publisher    = {{ACM}},
  title        = {{{An Architecture Supporting Configurable Autonomous Multimodal Joint-Attention-Therapy for Various Robotic Systems}}},
  doi          = {{10.1145/3536220.3558070}},
  year         = {{2022}},
}

@inproceedings{51347,
  author       = {{Schütze, Christian and Groß, André and Wrede, Britta and Richter, Birte}},
  booktitle    = {{INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION}},
  pages        = {{166--170}},
  publisher    = {{ACM}},
  title        = {{{Enabling Non-Technical Domain Experts to Create Robot-Assisted Therapeutic Scenarios via Visual Programming}}},
  doi          = {{10.1145/3536220.3558072}},
  year         = {{2022}},
}

@inproceedings{55337,
  abstract     = {{As AI is more and more pervasive in everyday life, humans have an increasing demand to understand its behavior and decisions. Most research on explainable AI builds on the premise that there is one ideal explanation to be found. In fact, however, everyday explanations are co-constructed in a dialogue between the person explaining (the explainer) and the specific person being explained to (the explainee). In this paper, we introduce a first corpus of dialogical explanations to enable NLP research on how humans explain as well as on how AI can learn to imitate this process. The corpus consists of 65 transcribed English dialogues from the Wired video series 5 Levels, explaining 13 topics to five explainees of different proficiency. All 1550 dialogue turns have been manually labeled by five independent professionals for the topic discussed as well as for the dialogue act and the explanation move performed. We analyze linguistic patterns of explainers and explainees, and we explore differences across proficiency levels. BERT-based baseline results indicate that sequence information helps predicting topics, acts, and moves effectively.}},
  author       = {{Wachsmuth, Henning and Alshomary, Milad}},
  booktitle    = {{Proceedings of the 29th International Conference on Computational Linguistics}},
  editor       = {{Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}},
  pages        = {{344–354}},
  publisher    = {{International Committee on Computational Linguistics}},
  title        = {{{“Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations}}},
  year         = {{2022}},
}

@inproceedings{34067,
  author       = {{Sengupta, Meghdut and Alshomary, Milad and Wachsmuth, Henning}},
  booktitle    = {{Proceedings of the 2022 Workshop on Figurative Language Processing}},
  title        = {{{Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning}}},
  year         = {{2022}},
}