@inbook{61323, author = {{Wrede, Britta and Buschmeier, Hendrik and Rohlfing, Katharina Justine and Booshehri, Meisam and Grimminger, Angela}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, pages = {{227--245}}, publisher = {{Springer}}, title = {{{Incremental communication}}}, doi = {{10.1007/978-981-96-5290-7_12}}, year = {{2026}}, } @inbook{65090, abstract = {{Abstract If XAI are to become social XAI, XAI methods must have capabilities enabling them to ‘extract’ information about the underlying AI model and to generate explanatory content based on that information. In a dialog between explainer and explainee, the explanans presented in every explanation move have to relate to each other understandably and coherently in order to remain trustworthy. This signifies that the generated explanantia have to be consistent—independently of what question is answered by each explanans, in what modality, in what vocabulary, and at what level of abstraction. Moreover, it is advantageous to be able to provide a rich palette of different kinds of explanantia in order to be able to have a fluent dialog in which the explanantia can be generated and adapted to the context, the explainee, feedback, reactions during the interaction with the explainee, and so forth. This chapter attempts to identify relevant questions that an explainee might ask during an explanatory dialog, and it assesses to what extent different XAI methods are capable of addressing these questions in a coherent way. The Contextual Importance and Utility (CIU) method is used to illustrate how an XAI method can generate explanantia for most of the identified questions. CIU also provides a flexibility in how explanatory content is generated that makes it possible to create a meaningful dialog with the explainee.}}, author = {{Främling, Kary and Thommes, Kirsten and Wrede, Britta}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Generation of Explanatory Content and Requirements for Social XAI}}}, doi = {{10.1007/978-981-96-5290-7_15}}, year = {{2026}}, } @inbook{65088, abstract = {{Abstract Quantitatively evaluating the benefits of eXplainable Artificial Intelligence (XAI) and social XAI for humans is not a trivial pursuit. Therefore, we categorize the potential measures in terms of subjective and objective outcomes and short- and long-term outcomes of interactive social XAI. When reviewing the current state of the art, we observed some measurement problems in the literature: (a) Researchers do not clearly state whether they want to measure the inner state of users, users’ behavioral response, or the overall AI-human collaborative performance. (b) Moreover, most measures implicitly assume that all humans either do not react or improve in attitudes or performance. Psychological reactance (feeling or doing the opposite) is usually not captured. (c) Many researchers invent their own scale when measuring psychological constructs, thereby jeopardizing the validity of their measures and slowing down progress in the field, because general evidence and subsequent learning can be achieved only by collecting many compatible pieces of evidence. (d) Most studies look into short-term outcomes and neglect that experiences in social interactions with XAI may evolve and have long-term outcomes not only for the individual but also for groups or society at large.}}, author = {{Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Measuring the Outcome of sXAI}}}, doi = {{10.1007/978-981-96-5290-7_28}}, year = {{2026}}, } @inbook{65086, abstract = {{Abstract Explainable AI (XAI) aims to make the decisions and behavior of an AI understandable to the people interacting with it and to those affected by its outcomes. To make XAI social, real-world XAI systems need to simulate not only the ways in which human explainers behave within explanatory dialogs but also the ways in which such dialogs can successfully achieve the intended understanding on the explainee’s side. This, in turn, requires an operationalization of the three core aspects of social XAI: multimodality, incrementality, and patterns. This chapter lays the ground for this goal by defining a basic operational model of social interactions that can be refined and extended to account for the specificities of any explanatory real-world setting. This serves as a basis for summarizing and discussing existing ideas from explainability research and related areas in order to operationalize each core aspect. Selected examples and case studies illustrate how to concretely realize such an operationalization, thereby serving as a starting point for future research on social interaction with XAI.}}, author = {{Wachsmuth, Henning and Thommes, Kirsten and Alshomary, Milad}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Operationalizing Social Interaction}}}, doi = {{10.1007/978-981-96-5290-7_27}}, year = {{2026}}, } @inbook{65091, abstract = {{Abstract This chapter examines key challenges and potential improvements in the areas of user interaction and dynamic explanations. It highlights the need for XAI systems to address context factors beyond their predefined scope, it points to the potential need to cocreate new concepts that are adapted to particular explainees, and it provides a clear overview of the XAI system’s underlying knowledge structure and interaction steps. Emphasis is placed on mixed-initiative interaction in which the system can lead or respond based on the context and the explainee’s reactions while asserting the importance of maintaining coherence across consecutive explanations. These advances aim to make XAI systems more flexible, interactive, and user-centric. An operationalization section outlines how such social XAI systems could be implemented based on the XAI capabilities provided by the Contextual Importance and Utility XAI method described in the previous chapter.}}, author = {{Främling, Kary and Wrede, Britta and Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Exploration of Explaining Content}}}, doi = {{10.1007/978-981-96-5290-7_16}}, year = {{2026}}, } @inbook{65087, abstract = {{Abstract Much research in XAI focuses on single, one-shot interactions, implicitly assuming that interactions have no past, no future, and no surroundings. Although this assumption may be necessary for many empirical research settings, it is overly simplifying and unrealistic. Whereas empirical research focuses on a world in which no social context exists, real applications are embedded in a temporal (past and future) and social context. Social science research shows that repeated interactions and secondhand knowledge in the social space massively affect human attitudes and behaviors. This chapter explains how not only repeated interactions between XAI and humans but also the social space and secondhand information may affect social XAI research.}}, author = {{Thommes, Kirsten and Främling, Kary and Wrede, Britta and Kubler, Sylvain}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Interaction History in Social XAI}}}, doi = {{10.1007/978-981-96-5290-7_17}}, year = {{2026}}, } @inbook{65089, abstract = {{Abstract In the past, there has been much research aiming to evaluate XAI practices—that is, explanations that can add to a user’s understanding of “why” or “why not.” However, because there is such a huge amount of diversity in social contexts, optimizing for the mean neglects the social dimensions of to whom, what, why, when, and where explanations are provided. Nonetheless, these dimensions matter. We give some brief examples on the accuracy of the mental model (as an example for who?), on measuring explanation practices (as an example of what?), on human motivation (as an example of why?), on repeated interactions (as an example of when), and on bystander effects (as an example of where?). Importantly, controlling for these factors (or randomizing them) is as important as attempting to perform external validations.}}, author = {{Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Evaluation Principles}}}, doi = {{10.1007/978-981-96-5290-7_26}}, year = {{2026}}, } @article{61819, author = {{Papenkordt, Jörg and Ngonga Ngomo, Axel-Cyrille and Thommes, Kirsten}}, issn = {{0144-929X}}, journal = {{Behaviour & Information Technology}}, pages = {{1--22}}, publisher = {{Informa UK Limited}}, title = {{{Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection?}}}, doi = {{10.1080/0144929x.2025.2568928}}, year = {{2025}}, } @inbook{61877, abstract = {{Abstract Research indicates that anger is a prevalent emotion in human-technology interactions, often leading to frustration, rejection and reduced trust, significantly impacting user experience and acceptance of technology. Particularly in high-risk or uncertain situations, where AI explanations are intended to help users make more informed decisions, decision-making is influenced by emotional factors, impairing understanding and leading to suboptimal choices. While XAI research continues to evolve, greater consideration of users’ emotions and individual characteristics remains necessary. Broadening empirical studies in this area could foster a more comprehensive understanding of decision-making processes following explanations, especially in relation to the interaction between emotions and cognition. In response, this study seeks to contribute to this area by employing an experimental design to examine the effects of AI explanations and emotion regulation on user reliance and trust of emotional users. The results provide a foundation for future human-centered research in XAI, focusing on the impact of emotions and cognition in human-technology interactions.}}, author = {{Lammert, Olesja}}, booktitle = {{Communications in Computer and Information Science}}, isbn = {{9783032083326}}, issn = {{1865-0929}}, publisher = {{Springer Nature Switzerland}}, title = {{{Can AI Regulate Your Emotions? An Empirical Investigation of the Influence of AI Explanations and Emotion Regulation on Human Decision-Making Factors}}}, doi = {{10.1007/978-3-032-08333-3_11}}, year = {{2025}}, } @article{61156, abstract = {{Explainability has become an important topic in computer science and artificial intelligence, leading to a subfield called Explainable Artificial Intelligence (XAI). The goal of providing or seeking explanations is to achieve (better) ‘understanding’ on the part of the explainee. However, what it means to ‘understand’ is still not clearly defined, and the concept itself is rarely the subject of scientific investigation. This conceptual article aims to present a model of forms of understanding for XAI-explanations and beyond. From an interdisciplinary perspective bringing together computer science, linguistics, sociology, philosophy and psychology, a definition of understanding and its forms, assessment, and dynamics during the process of giving everyday explanations are explored. Two types of understanding are considered as possible outcomes of explanations, namely enabledness, ‘knowing how’ to do or decide something, and comprehension, ‘knowing that’ – both in different degrees (from shallow to deep). Explanations regularly start with shallow understanding in a specific domain and can lead to deep comprehension and enabledness of the explanandum, which we see as a prerequisite for human users to gain agency. In this process, the increase of comprehension and enabledness are highly interdependent. Against the background of this systematization, special challenges of understanding in XAI are discussed.}}, author = {{Buschmeier, Hendrik and Buhl, Heike M. and Kern, Friederike and Grimminger, Angela and Beierling, Helen and Fisher, Josephine Beryl and Groß, André and Horwath, Ilona and Klowait, Nils and Lazarov, Stefan Teodorov and Lenke, Michael and Lohmer, Vivien and Rohlfing, Katharina and Scharlau, Ingrid and Singh, Amit and Terfloth, Lutz and Vollmer, Anna-Lisa and Wang, Yu and Wilmes, Annedore and Wrede, Britta}}, journal = {{Cognitive Systems Research}}, keywords = {{understanding, explaining, explanations, explainable, AI, interdisciplinarity, comprehension, enabledness, agency}}, title = {{{Forms of Understanding for XAI-Explanations}}}, doi = {{10.1016/j.cogsys.2025.101419}}, volume = {{94}}, year = {{2025}}, } @article{52202, author = {{Lammert, Olesja and Richter, Birte and Schütze, Christian and Thommes, Kirsten and Wrede, Britta}}, journal = {{Frontiers in Behavioral Economics}}, title = {{{Humans in XAI: Increased Reliance in Decision-Making Under Uncertainty by Using Explanation Strategies}}}, doi = {{10.3389/frbhe.2024.1377075}}, year = {{2024}}, } @inproceedings{55177, author = {{Thommes, Kirsten and Lammert, Olesja and Schütze, Christian and Richter, Birte and Wrede, Britta}}, booktitle = {{Communications in Computer and Information Science}}, isbn = {{9783031638022}}, issn = {{1865-0929}}, publisher = {{Springer Nature Switzerland}}, title = {{{Human Emotions in AI Explanations}}}, doi = {{10.1007/978-3-031-63803-9_15}}, year = {{2024}}, } @inproceedings{57250, author = {{Schütze, Christian and Richter, Birte and Lammert, Olesja and Thommes, Kirsten and Wrede, Britta}}, booktitle = {{HAI '24: Proceedings of the 12th International Conference on Human-Agent Interaction}}, isbn = {{9798400711787}}, pages = {{141--149}}, publisher = {{ACM}}, title = {{{Static Socio-demographic and Individual Factors for Generating Explanations in XAI: Can they serve as a prior in DSS for adaptation of explanation strategies?}}}, doi = {{10.1145/3687272.3688300}}, year = {{2024}}, } @inproceedings{55178, author = {{Thommes, Kirsten and Lammert, Olesja and Schütze, Christian and Richter, Birte and Wrede, Britta}}, title = {{{Human Emotions in AI Explanations}}}, year = {{2024}}, } @inproceedings{55403, abstract = {{In this paper we consider the interactive processes by which an explainer and an explainee cooperate to produce an explanation, which we refer to as co-construction. Explainable Artificial Intelligence (XAI) is concerned with the development of intelligent systems and robots that can explain and justify their actions, decisions, recommendations, and so on. However, the cooperative construction of explanations remains a key but under-explored issue. This short paper proposes an architecture for intelligent systems that promotes a co-constructive and interactive approach to explanation generation. By outlining its basic components and their specific roles, we aim to contribute to the advancement of XAI computational frameworks that actively engage users in the explanation process.}}, author = {{Buschmeier, Hendrik and Cimiano, Philipp and Kopp, Stefan and Kornowicz, Jaroslaw and Lammert, Olesja and Matarese, Marco and Mindlin, Dimitry and Robrecht, Amelie Sophie and Vollmer, Anna-Lisa and Wagner, Petra and Wrede, Britta and Booshehri, Meisam}}, booktitle = {{Proceedings of the 2024 Workshop on Explainability Engineering}}, location = {{Lisbon, Portugal}}, pages = {{20--25}}, publisher = {{ACM}}, title = {{{Towards a Computational Architecture for Co-Constructive Explainable Systems}}}, doi = {{10.1145/3648505.3648509}}, year = {{2024}}, } @inproceedings{48285, author = {{Lebedeva, Anastasia and Kornowicz, Jaroslaw and Lammert, Olesja and Papenkordt, Jörg}}, booktitle = {{Artificial Intelligence in HCI}}, title = {{{The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks}}}, doi = {{10.1007/978-3-031-35891-3_9}}, year = {{2023}}, } @article{49516, abstract = {{In this article, we present RISE—a Robotics Integration and Scenario-Management Extensible-Architecture—for designing human–robot dialogs and conducting Human–Robot Interaction (HRI) studies. In current HRI research, interdisciplinarity in the creation and implementation of interaction studies is becoming increasingly important. In addition, there is a lack of reproducibility of the research results. With the presented open-source architecture, we aim to address these two topics. Therefore, we discuss the advantages and disadvantages of various existing tools from different sub-fields within robotics. Requirements for an architecture can be derived from this overview of the literature, which 1) supports interdisciplinary research, 2) allows reproducibility of the research, and 3) is accessible to other researchers in the field of HRI. With our architecture, we tackle these requirements by providing a Graphical User Interface which explains the robot behavior and allows introspection into the current state of the dialog. Additionally, it offers controlling possibilities to easily conduct Wizard of Oz studies. To achieve transparency, the dialog is modeled explicitly, and the robot behavior can be configured. Furthermore, the modular architecture offers an interface for external features and sensors and is expandable to new robots and modalities.}}, author = {{Groß, André and Schütze, Christian and Brandt, Mara and Wrede, Britta and Richter, Birte}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{Artificial Intelligence, Computer Science Applications}}, publisher = {{Frontiers Media SA}}, title = {{{RISE: an open-source architecture for interdisciplinary and reproducible human–robot interaction research}}}, doi = {{10.3389/frobt.2023.1245501}}, volume = {{10}}, year = {{2023}}, } @article{51371, abstract = {{In this paper, we investigate the effect of distractions and hesitations as a scaffolding strategy. Recent research points to the potential beneficial effects of a speaker’s hesitations on the listeners’ comprehension of utterances, although results from studies on this issue indicate that humans do not make strategic use of them. The role of hesitations and their communicative function in human-human interaction is a much-discussed topic in current research. To better understand the underlying cognitive processes, we developed a human–robot interaction (HRI) setup that allows the measurement of the electroencephalogram (EEG) signals of a human participant while interacting with a robot. We thereby address the research question of whether we find effects on single-trial EEG based on the distraction and the corresponding robot’s hesitation scaffolding strategy. To carry out the experiments, we leverage our LabLinking method, which enables interdisciplinary joint research between remote labs. This study could not have been conducted without LabLinking, as the two involved labs needed to combine their individual expertise and equipment to achieve the goal together. The results of our study indicate that the EEG correlates in the distracted condition are different from the baseline condition without distractions. Furthermore, we could differentiate the EEG correlates of distraction with and without a hesitation scaffolding strategy. This proof-of-concept study shows that LabLinking makes it possible to conduct collaborative HRI studies in remote laboratories and lays the first foundation for more in-depth research into robotic scaffolding strategies.}}, author = {{Richter, Birte and Putze, Felix and Ivucic, Gabriel and Brandt, Mara and Schütze, Christian and Reisenhofer, Rafael and Wrede, Britta and Schultz, Tanja}}, issn = {{2414-4088}}, journal = {{Multimodal Technologies and Interaction}}, keywords = {{Computer Networks and Communications, Computer Science Applications, Human-Computer Interaction, Neuroscience (miscellaneous)}}, number = {{4}}, publisher = {{MDPI AG}}, title = {{{EEG Correlates of Distractions and Hesitations in Human–Robot Interaction: A LabLinking Pilot Study}}}, doi = {{10.3390/mti7040037}}, volume = {{7}}, year = {{2023}}, } @inproceedings{48280, author = {{Schütze, Christian and Lammert, Olesja and Richter, Birte and Thommes, Kirsten and Wrede, Britta}}, booktitle = {{Artificial Intelligence in HCI}}, title = {{{Emotional Debiasing Explanations for Decisions in HCI}}}, doi = {{10.1007/978-3-031-35891-3_20}}, year = {{2023}}, } @article{51348, abstract = {{AbstractWith 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.}}, 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}}, }