@article{65054, author = {{Jenert, Tobias and Kremer, H.-Hugo and Kückmann, Marie-Ann and Sänger, Niklas and Schmid, Leonie and Wilde, Stephanie}}, journal = {{bwp@ Spezial 23}}, title = {{{Kontextualisierung von Lehren und Lernen: Didaktische Einbettung und Implikationen eines virtuellen Berufskollegs zur Förderung von Professionalisierungsprozessen in der beruflichen Lehrkräftebildung}}}, year = {{2026}}, } @inbook{64735, author = {{Jenert, Tobias and Kremer, H.-Hugo and Kückmann, Marie-Ann and Sänger, Niklas and Schmid, Leonie and Wilde, Stephanie}}, booktitle = {{Handlungsorientierung in der Ausbildung von Lehrkräften und pädagogischen Fachkräften - Konzeptionen und Forschungsperspektiven}}, editor = {{Vogelsang, Christoph and Grotegut, Lea and Bruns, Julia and Fechner, Sabine}}, publisher = {{Waxmann}}, title = {{{Professionelle Entwicklung für das Lehramt an Berufskollegs. Theoretische Analysen besonderer Kompetenzanforderungen und Konsequenzen für die Studienganggestaltung}}}, doi = {{https://doi.org/10.31244/9783818851057}}, volume = {{2}}, year = {{2026}}, } @inbook{65061, abstract = {{Abstract One of the purposes for which XAI is often brought into play is to enable a user to act responsibly. However, responsibility is a complex normative and social phenomenon that we unfold in this chapter. We consider that the classical concepts of agency and responsibility do not fully capture what is needed for meaningful collaboration between human users and XAI. Advocating the perspective of sXAI, we argue that the growing adaptivity of AI systems will result in sXAI being considered as partners. Both partners adopt particular (dialogical) roles within a collaborative process and take responsibility for them. We expect that these roles lead to reactive attitudes toward the sXAI on the side of the human partners that make these roles relational. They resemble those reactive attitudes that we hold toward other human agents. For agents to exercise their responsibility, they need to possess agential capacities to fulfill their role with respect to the structure of a social interaction. Hence, sXAI can be expected to act responsibly. But because of XAI’s limited normative capacities, it might rather act as a marginal agent. We refer to marginal agents and show they can be scaffolded with regard to their agential capacities and their knowledge about the structure of a social interaction. The structure links the actions of the partners to each other in terms of a set of stimuli and responses to it in pursuit of a particular goal. Hence, it is important to differentiate between the different goals that a structure can impose for exercising responsibility. Therefore, we follow (Responsibility from the margins. Oxford University Press; 2015. https://doi.org/10.1093/acprof:oso/9780198715672.24001.0001 ) and offer three structures that can help to organize responsibility for decisions made with the assistance of AI systems. These structures are attributability, answerability, and accountability. Our insights will inform the development and design process of XAI to meet the guiding principles of responsible research and innovation as well as trustworthy AI. }}, author = {{Rohlfing, Katharina J. and Alpsancar, Suzana and Schulte, Carsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, pages = {{157--177}}, publisher = {{Springer Nature Singapore}}, title = {{{Responsibilities in sXAI}}}, doi = {{10.1007/978-981-96-5290-7_9}}, year = {{2026}}, } @inbook{65063, abstract = {{Abstract This chapter critically examines how social explainable AI (sXAI) can better support AI practitioners in ensuring fairness in AI-based decision-making. We argue for a fundamental shift: Fairness should be understood not as a technical property or an information problem, but as a matter of vulnerability—focusing on the real-world impacts of AI on individuals and groups, especially those most at risk. Hereby, we call for a shift in perspective: from fair AI to tasking AI fairly . To motivate our vulnerability approach, we review the “Dutch welfare fraud scandal” (system risk indication—SyRI) and current challenges in the field of fair AI/machine learning (ML). Vulnerability of a person or members of a definable group of persons is a complex relational notion, and not a technical property of a technical system. Accordingly, we suggest several nontechnical strategies that hold the promise to compensate for the insufficiency of purely technical approaches to fairness and other ethical issues in the practical use of AI-based systems. To discuss how sXAI, due to its interactive and adaptive social character, might better fulfill this role than current XAI techniques, we provide a toy scenario for how sXAI might support the virtuous AI practitioner in an ethical inquiry. Finally, we also address challenges and limits of our approach. }}, author = {{Alpsancar, Suzana and Stamboliev, Eugenia}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, pages = {{557--581}}, publisher = {{Springer Nature Singapore}}, title = {{{Tasking AI Fairly. How to Empower AI Practitioners With sXAI?}}}, doi = {{10.1007/978-981-96-5290-7_29}}, year = {{2026}}, } @inbook{65064, abstract = {{Abstract XAI can minimize the risks of being manipulated and deceived by AI but in turn entails other specific risks. This also applies to sXAI, and the specifically social character of sXAI harbors particular risks that designers and developers should be aware of. In this chapter, we shall discuss the potential opportunities and risks of sXAI. We see a particularly positive potential in the social character of sXAI, which lies in the fact that skillful users, including those with “healthy distrust,” can use the adaptivity of sXAI to produce an explanation that is actually relevant and adequate for them. However, this requires a high level of skills on the part of the user and is thus in contrast to the general promise of efficiency in the use of AI. A potential risk of XAI is that it can be (even more) persuasive, as the interactive involvement and the anthropomorphism strengthen a trustworthy appearance/performance (independent of the adequacy of the sXAI performance).}}, author = {{Alpsancar, Suzana and Klenk, Michael}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, pages = {{583--616}}, publisher = {{Springer Nature Singapore}}, title = {{{The Risk of Manipulation and Deception in sXAI}}}, doi = {{10.1007/978-981-96-5290-7_30}}, year = {{2026}}, } @inbook{62709, author = {{Reijers, Wessel and Alpsancar, Suzana}}, booktitle = {{Social explainable AI. Communications of NII Shonan Meetings}}, editor = {{Rohlfing, Katharina and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kirsten}}, pages = {{179--195}}, publisher = {{Springer}}, title = {{{Values and Norms in sXAI}}}, year = {{2026}}, } @book{57167, editor = {{Alpsancar, Suzana and Friedrich, Alexander and Gehring, Petra and Kaminski, Andreas and Nordmann, Alfred}}, isbn = {{978-3-7560-1830-7}}, publisher = {{Nomos}}, title = {{{Jahrbuch Technikphilosophie. Täuschung und Illusion. 10. Jahrgang 2025}}}, year = {{2026}}, } @book{65065, abstract = {{Abstract This introduction sets the stage for the present book. Whereas research in eXplainable AI (XAI) is motivated by societal changes and values, technology development largely ignores social aspects. This book aims to address this research gap with a systematic and comprehensive social view on explainable AI. Besides introducing many relevant concepts, the book offers first access to their possible implementation, thus advancing the development of more social XAI. The introduction starts by connecting the topic to the general research field of XAI. The second part defines the novel approach of social eXplainable AI (sXAI) along the three characteristics of social interaction such as patternedness, incrementality, and multimodality. Finally, the third part explains the structure followed by each chapter. The book offers insights not only for readers who work on technology development but also for those working in sociotechnical fields. Addressing an interdisciplinary readership, the book is an invitation for more exchange and further development of the sXAI field.}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kirsten}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Social Explainable AI}}}, doi = {{10.1007/978-981-96-5290-7_1}}, year = {{2026}}, } @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{61321, author = {{Grimminger, Angela and Buschmeier, Hendrik}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, pages = {{351--365}}, publisher = {{Springer}}, title = {{{Theoretical aspects of multimodal processing}}}, doi = {{10.1007/978-981-96-5290-7_18}}, year = {{2026}}, } @article{65066, abstract = {{Abstract We investigate whether the recently approved reforms of the apportionment of parliamentary seats to parties in the German Bundestag affects the parties’ political influence measured by power indices. We find that under neither reform the underlying simple game, which describes the possibilities to form governments, remains unchanged and as a result the Shapley-Shubik and the Banzhaf index are unaltered. As a consequence, the major change resulting from the reforms is the reduction of the Bundestag’s size to 630 seats.}}, author = {{Duman, Papatya and Haake, Claus-Jochen}}, issn = {{0948-5139}}, journal = {{Review of Economics}}, keywords = {{Bundestag reform, Banzhaf power index, Shapley-Shubik power index}}, number = {{3}}, pages = {{241--270}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{A Note on the Size Reduction Reform in the German Parliament: A Game Theoretic Analysis of Power Indices}}}, doi = {{10.1515/roe-2024-0048}}, volume = {{76}}, year = {{2026}}, } @inbook{64624, author = {{Lehberger, Regine}}, booktitle = {{Handlungsorientierung in der Ausbildung von Fachkräften und pädagogischen Fachkräften. Konzeptionen und Forschungsperspektiven}}, editor = {{Vogelsang, Christoph and Grotegut, Lea and Bruns, Julia and Riese, Josef and Sabine, Fechner}}, pages = {{185--193}}, publisher = {{Waxmann}}, title = {{{Reflexion von individuellen Selbstregulationsfähigkeiten zur Professionalisierung im bildungswissenschaftlichen Begleitseminar des Praxissemesters}}}, volume = {{2}}, year = {{2026}}, } @inbook{61322, author = {{Lazarov, Stefan Teodorov and Tchappi, Igor 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 = {{367--390}}, publisher = {{Springer}}, title = {{{Characteristics of nonverbal behavior}}}, doi = {{10.1007/978-981-96-5290-7_19}}, year = {{2026}}, } @inbook{61324, author = {{Wagner, Petra and Kopp, Stefan}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, pages = {{433--446}}, publisher = {{Springer}}, title = {{{Timing and synchronization of multimodal signals in explanations}}}, doi = {{10.1007/978-981-96-5290-7_22}}, year = {{2026}}, } @inbook{61112, author = {{Rohlfing, Katharina J. and Vollmer, Anna-Lisa and Grimminger, Angela}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina and Främling, Kary and Thommes, Kirsten and Alpsancar, Suzana and Lim, Brian Y.}}, publisher = {{Springer}}, title = {{{Practices: How to establish an explaining practice}}}, doi = {{10.1007/978-981-96-5290-7_5}}, year = {{2026}}, } @inbook{65069, author = {{Främling, Kary and Alami, Rachid and Hulstijn, Joris and Tchappi, Igor and Grimminger, Angela and Wrede, Britta and Buschmeier, Hendrik and Kubler, Sylvain}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Alpsancar, Suzana and Thommes, Kirsten and Lim, Brian Y.}}, isbn = {{9789819652891}}, pages = {{19--38}}, publisher = {{Springer}}, title = {{{Scenarios of Social Explainable AI in practice}}}, doi = {{10.1007/978-981-96-5290-7_2}}, year = {{2026}}, } @inproceedings{63031, author = {{Menne, Anna Lena and Schulz, Christian}}, booktitle = {{HCI International 2026 Posters: 28th International Conference on Human-Computer Interaction, HCI 2026, Montreal, Canada, July 26-31, 2026, Proceedings}}, editor = {{Stephanidis, Constantine and Antona, Margherita and Ntoa, Stavroula and Salvendy, Gavriel}}, location = {{Montreal}}, publisher = {{Springer International Publishing}}, title = {{{Social Context in Human-AI Interaction (HAI): Towards a Theoretical Framework Based on Multi-Perspectival Imaginaries}}}, year = {{2026}}, } @inbook{59747, author = {{Schulz, Christian}}, booktitle = {{Tausend Plattformen: Plattformforschung nach dem Digital Services Act}}, editor = {{Eickelmann, Jennifer and Mämecke, Thorben and Ruschemeier, Hannah and Waldmann, Maximilian}}, publisher = {{Büchner Verlag}}, title = {{{Das Imaginäre der Plattformen: Soziale Medien als Infrastrukturen der Anerkennung}}}, year = {{2026}}, } @article{64789, author = {{Beer, Fabian and Schulz, Christian}}, journal = {{RESET Journal (Recherches sciences sociale sur internet) Special Issue: Towards New Social and Historical Studies of Artificial Intelligence}}, publisher = {{Open Edition Journals}}, title = {{{AI has never been “inherently interpretable”: On a paradoxical origin of eXplainable AI (XAI)}}}, year = {{2026}}, } @unpublished{65073, abstract = {{We study the large-time behavior of the continuous-time heat kernel and of solutions to the heat equation on homogeneous trees. First, we derive sharp asymptotic formulas for the heat kernel as $t\to\infty$. Second, using them, we show that solutions with initial data in weighted $\ell^1$ classes, asymptotically factorize in $\ell^p$ norms, $p\in[1,\infty]$, as the product of the heat kernel, times a $p$-mass function, dependent on the initial condition and $p$. The $p$-mass function is described in terms of boundary averages associated with Busemann functions for $p<2$, while for $p\ge 2$, it is expressed through convolution with the ground spherical function. For comparison, the case of the integers shows that a single constant mass determines the asymptotics of solutions to the heat equation for all $p$, emphasizing the influence of the graph geometry on heat diffusion.}}, author = {{Papageorgiou, Efthymia}}, booktitle = {{2603.11232}}, title = {{{Long-time asymptotics for the heat kernel and for heat equation solutions on homogeneous trees}}}, year = {{2026}}, }