@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{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}}, } @inproceedings{65494, author = {{Manzoor, Ali and Zahera, Hammada M. and Saleem, Muhammad and Mahmood, Yasir and Speck, René and Khan, Hashim and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings}}, keywords = {{dice hamada hashim kiakadamie mahmood manzoor ngonga rene sail saleem}}, title = {{{Document-level Relation Extraction using Reinforcement Learning with Knowledge Graph Feedback}}}, year = {{2026}}, } @misc{66090, author = {{Thomas, Sven}}, booktitle = {{Jahrbuch Technikphilosophie}}, publisher = {{Nomos}}, title = {{{Discourses on Discursive Machines. Review of Mark Coeckelbergh and David J. Gunkel, Communicative AI: A Critical Introduction to Large Language Models}}}, volume = {{11}}, year = {{2026}}, } @inproceedings{67285, abstract = {{This research provides a method for designing competenceoriented, modular workflows that support strategic decisionmaking in strategic product planning (SPP) under conditions of uncertainty and time pressure. Increasing digitalization and data availability require decision support systems that integrate analytical models while being tailored to the competencies of decision-makers. Based on a systematic literature review and stakeholder workshops, eight success factors for competency-oriented workflow integration are identified, including modularity, traceability, interoperability, adaptability, handling uncertainty, and user acceptance. Based on these factors, a five-step method is developed that links business processes, data sources, evaluation criteria, visualization formats, and graphical workflow implementation. Strategic and technical decision logic are integrated into a unified, modular framework using graphical modeling environments. The method is validated in workshops and demonstrates its applicability in time-critical, multi-criteria decision contexts. The results show improved transparency, adaptability, and practical usability of datadriven decision processes that support robust and traceable business decisions. }}, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{24th INTERNATIONAL INDUSTRIAL SIMULATION CONFERENCE }}, editor = {{Gonzalez, Jose David Nunez and Romay, Manuel Grana}}, isbn = {{978-9-492859-40-2}}, keywords = {{Workflow, Decision Support, Strategic Product Planning, Decision-making}}, location = {{San Sebastian, Spain}}, pages = {{73--80}}, title = {{{Competence-Oriented Modular Workflow Design for Strategic Decisions}}}, volume = {{24}}, year = {{2026}}, } @inproceedings{67284, abstract = {{For foresightful strategic decisions in engineering and production, it is important to identify and classify operational engineering data inmanufacturing companies in terms of relevance and impact. Established methods like Scenario-Technique or roadmapping consider expertise of strategic decision-makers and central decision-making bodies by, for instance, workshops and interviews. There is no overarching approach for evaluating the variety of operational data sources and feed them into specific data processing pipelines targeting strategic decision support. Based on a systematic literature analysis and comprehensive industrial experience, requirements are derived. These requirements are used to develop a method to identify, classify and evaluate data for strategic decision-making. The method is validated in an industrial project. The aim is to exploit informal and fragmented data from operational levels into structured, transparent decision support for strategic players.}}, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{AHFE International}}, issn = {{2771-0718}}, location = {{Paris}}, publisher = {{AHFE International}}, title = {{{Extended Exploitation of Data in Manufacturing Companies for Strategic Decision-Making}}}, doi = {{10.54941/ahfe1008175}}, volume = {{237}}, year = {{2026}}, } @inproceedings{67286, abstract = {{The effects of decisions in strategic product planning are usually far-reaching, but at the same time difficult to assess. Simulations help to make these effects more tangible. Until now, the use of simulation in strategic product planning has been limited to behavioral and innovation diffusion studies. This paper presents a process model for widening business models or planning upcoming product generations. It supports integrating the simulation of technical systems into decisionmaking processes to select the most suitable product for adaptation to new application scenarios. To this end, the characteristics of such new application scenarios are applied to existing product models in simulation, and the system behavior is evaluated based on key figures. This supports the comparison of different products and the selection of the product alternative requiring the least amount of modification. The process model enables decision-makers to systematically integrate simulation into the decision-making process and thus make more data-based decisions. }}, author = {{Gräßler, Iris and Özcan, Deniz and Döhner, Niklas}}, booktitle = {{24th INTERNATIONAL INDUSTRIAL SIMULATION CONFERENCE }}, editor = {{Gonzalez, Jose David Nunez and Romay, Manuel Grana}}, isbn = {{978-9-492859-40-2}}, keywords = {{Decision-making, Decision support systems, Mechanical engineering, System analysis, System engineering}}, location = {{San Sebastian, Spain}}, pages = {{65--72}}, title = {{{Leveraging System Simulation for Decision Support in Strategic Product Planning}}}, volume = {{24}}, year = {{2026}}, } @inbook{67159, author = {{Alpsancar, Suzana}}, booktitle = {{Das 20. Jahrhundert in Grundbegriffen. Lexikon zur historischen Semantik in Deutschland}}, editor = {{Müller, Ernst and Picht, Barbara and Schmieder, Falko}}, publisher = {{Schwabe Verlag}}, title = {{{Computer, Digitalisierung}}}, doi = {{10.31267/grundbegriffe_21465309}}, year = {{2026}}, } @article{66665, abstract = {{Multimode quantum light has promising applications in many areas of physics, such as quantum communications and quantum computing. However, its multimode nature also makes it challenging to measure its properties. Recently [I. Barakat et al., Optica Quantum 3, 36 (2025)], a technique for the simultaneous measurement of squeezing of multiple broadband modes based on a phase-sensitive amplification approach was experimentally implemented using a setup that effectively corresponds to an SU(1,1) interferometer. Here, we aim to provide a complete theoretical analysis of the modal structure of (generally unbalanced) SU(1,1) interferometers and a detailed theoretical formal derivation of the framework for this technique. Utilizing the joint Schmidt decomposition of the transfer functions, we investigate the shape and phase profiles of the modes of the SU(1,1) interferometer and its components [parametric down-conversion (PDC) sections] for different parametric gain regimes. We discover a complicated interplay between the PDC modes and the modes of the entire interferometer, and analyze it by using their overlap coefficients as a similarity measure. Finally, we develop a rigorous processing method for the aforementioned multimode squeezing measurement technique and discuss necessary approximations to make this method experimentally feasible.}}, author = {{Scharwald, Dennis and Sharapova, Polina}}, issn = {{2643-1564}}, journal = {{Physical Review Research}}, number = {{3}}, publisher = {{American Physical Society (APS)}}, title = {{{Characterization of spatial Schmidt modes in high-gain SU(1,1) interferometers}}}, doi = {{10.1103/ph64-ts39}}, volume = {{8}}, year = {{2026}}, } @inproceedings{67295, abstract = {{In increasingly volatile and uncertain markets, corporate resilience has become a critical capability in strategic product planning. Companies face significant challenges in systematically monitoring and interpreting heterogeneous environmental data originating from diverse sources, formats, and temporal contexts. While predefined workflows and decision trees can support strategic analysis, they often lack the flexibility required to cope with dynamic market conditions and foresightrelated data from extreme dispersed and heterogeneous sources. This paper proposes a method to enhance corporate resilience through the application of generic, reusable AI-based workflows in strategic product planning. The approach integrates Data Science and Artificial Intelligence methods into modular, visually modelled workflows that enable hybrid human-AI decision-making. Based on a systematic literature review and an analysis of industrial challenges, key success factors and resilience criteria are identified. These insights are used to develop a method that supports internal and external analyses, scenario-based strategy development, and adaptive implementation monitoring within a generic workflow structure. The method leverages techniques such as machine learning and generative AI to process structured and unstructured data, identify patterns, and support real-time strategic assessments. Validation with decision-makers from medium-sized companies demonstrates improved transparency, repeatability, and cross-functional collaboration compared to predefined workflows.}}, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}}, editor = {{Gräßler, Iris}}, location = {{Paderborn}}, publisher = {{LibreCat University}}, title = {{{Corporate resilience through generic AI-based workflows in strategic product planning}}}, doi = {{10.17619/UNIPB/1-2636}}, volume = {{1}}, year = {{2026}}, } @misc{67517, author = {{Fahimi, Miriam}}, booktitle = {{A&W Blog}}, title = {{{ Fünf Forder­ungen für eine sozial-­ökologische Rechen­zentrums­politik}}}, year = {{2026}}, } @misc{67511, author = {{Fahimi, Miriam}}, booktitle = {{Berliner Gazette}}, title = {{{Are Data Centers Factories? Industrial Promises and Socio-Ecological Losses}}}, year = {{2026}}, } @misc{67513, author = {{Fahimi, Miriam}}, booktitle = {{Frauensolidarität}}, number = {{176}}, title = {{{Ist die beste KI vielleicht keine KI?}}}, year = {{2026}}, } @misc{67514, author = {{Fahimi, Miriam}}, booktitle = {{Mosaik}}, title = {{{Luftschlösser und Rechenzentren}}}, year = {{2026}}, } @misc{67515, author = {{Fahimi, Miriam}}, booktitle = {{A&W Blog}}, title = {{{Thermischer Teufelskreis der KI}}}, year = {{2026}}, } @misc{67512, author = {{Fahimi, Miriam}}, booktitle = {{Platypus CASTAC}}, title = {{{Can a Data Center Sweat?}}}, year = {{2026}}, } @misc{67510, booktitle = {{Journal für Entwicklungspolitik}}, editor = {{Nora, Krenmayr and Martin, Cerny}}, issn = {{0258-2384}}, number = {{42}}, pages = {{154--164}}, publisher = {{Mattersburger Kreis}}, title = {{{Interview: Der saure Regen aus der Cloud. Rechenzentren als Geopolitik}}}, doi = {{10.20446/jep-2414-3197-42-2-154}}, volume = {{42}}, year = {{2026}}, }