@article{59756, abstract = {{A current concern in the field of Artificial Intelligence (AI) is to ensure the trustworthiness of AI systems. The development of explainability methods is one prominent way to address this, which has often resulted in the assumption that the use of explainability will lead to an increase in the trust of users and wider society. However, the dynamics between explainability and trust are not well established and empirical investigations of their relation remain mixed or inconclusive. In this paper we provide a detailed description of the concepts of user trust and distrust in AI and their relation to appropriate reliance. For that we draw from the fields of machine learning, human–computer interaction, and the social sciences. Based on these insights, we have created a focused study of empirical literature of existing empirical studies that investigate the effects of AI systems and XAI methods on user (dis)trust, in order to substantiate our conceptualization of trust, distrust, and reliance. With respect to our conceptual understanding we identify gaps in existing empirical work. With clarifying the concepts and summarizing the empirical studies, we aim to provide researchers, who examine user trust in AI, with an improved starting point for developing user studies to measure and evaluate the user’s attitude towards and reliance on AI systems.}}, author = {{Visser, Roel and Peters, Tobias Martin and Scharlau, Ingrid and Hammer, Barbara}}, issn = {{1389-0417}}, journal = {{Cognitive Systems Research}}, keywords = {{XAI, Appropriate trust, Distrust, Reliance, Human-centric evaluation, Trustworthy AI}}, publisher = {{Elsevier BV}}, title = {{{Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation}}}, doi = {{10.1016/j.cogsys.2025.101357}}, year = {{2025}}, } @inproceedings{59999, author = {{Rautenberg, Frederik and Kuhlmann, Michael and Seebauer, Fritz and Wiechmann, Jana and Wagner, Petra and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, location = {{Hyderabad, India }}, publisher = {{IEEE}}, title = {{{Speech Synthesis along Perceptual Voice Quality Dimensions}}}, doi = {{10.1109/icassp49660.2025.10888012}}, year = {{2025}}, } @article{59755, abstract = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine, trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception, for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated classification as advice. Research to improve such interactions often aims to foster the user’s trust, which in turn should improve the combined human-AI performance. Given that AI models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is an equally interesting target of research. We created two image classification scenarios in which the participants received mock-up AI advice. The quality of the advice decreases for a phase of the experiment. We studied the task performance, trust and distrust of the participants, and tested whether an instruction to remain skeptical and review each piece of advice led to a better performance compared to a neutral condition. Our results indicate that this instruction does not improve but rather worsens the participants’ performance. Repeated single-item self-report of trust and distrust shows an increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether the problems of under- and over-reliance have been mitigated. We discuss implications of our results for the usage of disclaimers before interacting with AI, as prominently used in current LLM-based chatbots, and for trust and distrust research.}}, author = {{Peters, Tobias Martin and Scharlau, Ingrid}}, journal = {{Frontiers in Psychology}}, keywords = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}}, title = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}}, doi = {{10.3389/fpsyg.2025.1574809}}, volume = {{16}}, year = {{2025}}, } @inproceedings{61505, author = {{Yigitbas, Enes and Kröker, Leon}}, booktitle = {{Proceedings of the 17th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2025)}}, publisher = {{Springer}}, title = {{{Bridging the Reality-Virtuality Gap: Evaluating Attention Markers in a Cross-Reality Application for Improved User Coordination}}}, year = {{2025}}, } @inproceedings{61503, author = {{Yigitbas, Enes and Agha, Ali Amin}}, booktitle = {{Proceedings of the 17th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2025) }}, publisher = {{Springer}}, title = {{{Gamification and Physiological Feedback: A Novel Approach to Reducing Glossophobia in Virtual Reality Environments}}}, year = {{2025}}, } @inproceedings{61506, author = {{Karakaya, Kadiray and Klauke, Jonas and Yigitbas, Enes}}, booktitle = {{Proceedings of the 3rd International Workshop on Virtual and Augmented Reality Software Engineering (VARSE) @ ASE 2025 }}, title = {{{Toward Static Analysis of Immersive Attacks}}}, year = {{2025}}, } @inproceedings{59836, author = {{Krois, Sebastian and Scharke, Kevin and Yigitbas, Enes and Oevel, Gudrun}}, booktitle = {{10th International Symposium on End-User Development (IS-EUD 2025) }}, title = {{{A VR End-User Development Toolbox for Media Study Students - An Initial Experience Report}}}, year = {{2025}}, } @book{63713, author = {{Linssen, Oliver and Volland, Alexander and Yigitbas, Enes and Engstler, Martin and Bertram, Martin and Hanser, Eckhart and Kalenborn, Axel}}, title = {{{Projektmanagement Und Vorgehensmodelle 2025 – Post-Agilität, Resilienz, Transformation}}}, year = {{2025}}, } @article{60017, author = {{Skolik, Alexander Marcus and zur Heiden, Philipp and Donner, Johannes Aurelius Tamino and Priefer, Jennifer}}, journal = {{ECIS 2025 Proceedings}}, location = {{Amman, Jordan}}, title = {{{Igniting Knowledge Management for Assistance Systems in Maintenance: A Method for Knowledge Gathering}}}, volume = {{2}}, year = {{2025}}, } @article{63744, abstract = {{Orbital angular momentum (OAM) modes are an important resource used in various branches of quantum science and technology due to their unique helical structure and countably infinite basis. Generating light that simultaneously carries high-order orbital angular momenta and exhibits quantum correlations is a challenging task. In this work, we present a theoretical approach to the generation of correlated Schmidt modes carrying OAM via parametric down-conversion (PDC) in cascaded nonlinear systems (nonlinear interferometers) pumped by Laguerre–Gaussian beams. We demonstrate how the number of generated modes and their population can be controlled by varying the pump parameters, the gain of the PDC process, and the distance between the crystals. We investigate the angular displacement measurement uncertainty of these interferometers and demonstrate that it can overcome the classical shot noise limit.}}, author = {{Scharwald, Dennis and Gehse, Lucas and Sharapova, Polina}}, issn = {{2378-0967}}, journal = {{APL Photonics}}, number = {{1}}, publisher = {{AIP Publishing}}, title = {{{Schmidt modes carrying orbital angular momentum generated by cascaded systems pumped with Laguerre–Gaussian beams}}}, doi = {{10.1063/5.0229802}}, volume = {{10}}, year = {{2025}}, } @article{63745, abstract = {{Multimode squeezed light is an increasingly popular tool in photonic quantum technologies, including sensing, imaging, and computation. Meanwhile, the existing methods of its characterization are technically complicated, which reduces the level of squeezing, and mostly deal with a single mode at a time. Here, for the first time, to the best of our knowledge, we employ optical parametric amplification to characterize multiple squeezing eigenmodes simultaneously. We retrieve the shapes and squeezing degrees of all modes at once through direct detection followed by modal decomposition. This method is tolerant to inefficient detection and does not require a local oscillator. For a spectrally and spatially multimode squeezed vacuum, we characterize eight strongest spatial modes, obtaining squeezing and anti-squeezing values of up to −5.2 ± 0.2 dB and 8.6 ± 0.3 dB, respectively, despite the 50% detection loss. This work, being the first exploration of an optical parametric amplifier’s multimode capability for squeezing detection, paves the way for the real-time detection of multimode squeezing.}}, author = {{Barakat, Ismail and Kalash, Mahmoud and Scharwald, Dennis and Sharapova, Polina and Lindlein, Norbert and Chekhova, Maria}}, issn = {{2837-6714}}, journal = {{Optica Quantum}}, number = {{1}}, publisher = {{Optica Publishing Group}}, title = {{{Simultaneous measurement of multimode squeezing through multimode phase-sensitive amplification}}}, doi = {{10.1364/opticaq.524682}}, volume = {{3}}, year = {{2025}}, } @article{61825, abstract = {{Abstract Industrial x-ray computed tomography (CT) systems with high geometric flexibility are increasingly utilized for large-scale measurement objects or challenging measurement tasks. To maintain high accuracy when deviating from the established circular scan trajectory, trajectory calibration methods using multi-sphere reference objects with known marker positions are commonly employed. These multi-sphere objects can either be scanned together with the measurement object (online trajectory calibration) or in a separate scan (offline trajectory calibration). While offline calibration increases machine time, it generally results in higher scan quality. However, a sufficient pose repeatability is necessary to ensure comparable or even superior accuracy to online calibration. In this contribution, we present a straightforward procedure to compare both types of trajectory calibration in a way that the differences of the results can directly be traced back to the influence of the pose repeatability. The multi-sphere reference object is not only used for trajectory calibration, but simultaneously as a measurement object for repeated measurements. The methodology is tested on both a twin robotic CT system and a conventional CT system that is additionally equipped with a hexapod manipulator for adaptive object tilting. Results showed, independent from the type of trajectory calibration, systematic measurement errors in the order of 10−5–10−4 of measured sphere distances and sphericity values below 50 μ m . For sphere distances, random errors were increased by a factor of 5 due to the offline trajectory calibration, but were still low ( < 1 μ m ) in comparison to systematic errors and the spread of different measurement features. Overall, both investigated systems demonstrated sufficient positioning repeatability for offline trajectory calibration. The method is in general also applicable to any other types of manipulator systems used for CT devices. It provides a workflow for the decision which type of trajectory calibration is preferable for a given CT system.}}, author = {{Butzhammer, Lorenz and Handke, Niklas and Wittl, Simon and Herl, Gabriel and Hausotte, Tino}}, issn = {{0957-0233}}, journal = {{Measurement Science and Technology}}, number = {{2}}, publisher = {{IOP Publishing}}, title = {{{Direct assessment of the influence of pose repeatability on the accuracy of dimensional measurements for computed tomography systems with high degrees of freedom}}}, doi = {{10.1088/1361-6501/ada05a}}, volume = {{36}}, year = {{2025}}, } @inbook{61150, abstract = {{Since the emergence of the field of eXplainable Artificial Intelligence (XAI), a growing number of researchers have argued that XAI should consider insights from the social sciences in order to adapt explanations to the expectations and needs of human users. This has led to the emergence of a field called Social XAI, which is concerned with understanding how explanations are actively shaped in the interaction between a human user and an AI system. Recognizing this turn in XAI toward making XAI systems more “social” by providing explanations that focus on human information needs and incorporating insights from human–human explanatory interactions, in this paper we provide a formal foundation for Social XAI. We do so by proposing novel ontological accounts of the key terms used in Social XAI based on Basic Formal Ontology (BFO). Specifically, we provide novel ontological accounts for explanandum, explanans, understanding, explanation, explainer, explainee, and context. In doing so, we discuss multifaceted entities in Social XAI (having both continuant and occurrent facets; e.g., explanation) and the relationship between understanding and explanation. Additionally, we propose solutions to seemingly paradoxical views on some terms (e.g., social constructivist vs. individual constructivist perspective on explanandum).}}, author = {{Booshehri, Meisam and Buschmeier, Hendrik and Cimiano, Philipp}}, booktitle = {{Proceedings of the 15th International Conference on Formal Ontology in Information Systems}}, isbn = {{9781643686172}}, issn = {{0922-6389}}, location = {{Catania, Italy}}, pages = {{255–268}}, publisher = {{IOS Press}}, title = {{{A BFO-based ontological analysis of entities in Social XAI}}}, doi = {{10.3233/faia250498}}, year = {{2025}}, } @inproceedings{61229, author = {{Muschalik, Maximilian and Fumagalli, Fabian and Frazzetto, Paolo and Strotherm, Janine and Hermes, Luca and Sperduti, Alessandro and Hüllermeier, Eyke and Hammer, Barbara}}, booktitle = {{The Thirteenth International Conference on Learning Representations (ICLR)}}, title = {{{Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks}}}, year = {{2025}}, } @inproceedings{61232, author = {{Visser, Roel and Fumagalli, Fabian and Hüllermeier, Eyke and Hammer, Barbara}}, booktitle = {{Proceedings of the European Symposium on Artificial Neural Networks (ESANN)}}, keywords = {{FF}}, title = {{{Explaining Outliers using Isolation Forest and Shapley Interactions}}}, year = {{2025}}, } @inproceedings{61231, author = {{Fumagalli, Fabian and Muschalik, Maximilian and Hüllermeier, Eyke and Hammer, Barbara and Herbinger, Julia}}, booktitle = {{Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS)}}, pages = {{5140--5148}}, publisher = {{PMLR}}, title = {{{Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory}}}, volume = {{258}}, year = {{2025}}, } @inproceedings{61153, author = {{Booshehri, Meisam and Buschmeier, Hendrik and Cimiano, Philipp}}, booktitle = {{Abstracts of the 3rd TRR 318 Conference: Contextualizing Explanations}}, location = {{Bielefeld, Germany}}, title = {{{A BFO-based ontology of context for Social XAI}}}, year = {{2025}}, } @inproceedings{61234, abstract = {{The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee's background and needs, recent research focused on co-constructive explanation dialogues, where an explainer continuously monitors the explainee's understanding and adapts their explanations dynamically. We investigate the ability of large language models (LLMs) to engage as explainers in co-constructive explanation dialogues. In particular, we present a user study in which explainees interact with an LLM in two settings, one of which involves the LLM being instructed to explain a topic co-constructively. We evaluate the explainees' understanding before and after the dialogue, as well as their perception of the LLMs' co-constructive behavior. Our results suggest that LLMs show some co-constructive behaviors, such as asking verification questions, that foster the explainees' engagement and can improve understanding of a topic. However, their ability to effectively monitor the current understanding and scaffold the explanations accordingly remains limited.}}, author = {{Fichtel, Leandra and Spliethöver, Maximilian and Hüllermeier, Eyke and Jimenez, Patricia and Klowait, Nils and Kopp, Stefan and Ngonga Ngomo, Axel-Cyrille and Robrecht, Amelie and Scharlau, Ingrid and Terfloth, Lutz and Vollmer, Anna-Lisa and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue}}, publisher = {{Association for Computational Linguistics}}, title = {{{Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues}}}, year = {{2025}}, } @inproceedings{59856, abstract = {{Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the success of techniques depends on various parameters, such as the task, language model, and context provided. Finding an effective prompt is, therefore, often a trial-and-error process. Most existing approaches to automatic prompting aim to optimize individual techniques instead of compositions of techniques and their dependence on the input. To fill this gap, we propose an adaptive prompting approach that predicts the optimal prompt composition ad-hoc for a given input. We apply our approach to social bias detection, a highly context-dependent task that requires semantic understanding. We evaluate it with three large language models on three datasets, comparing compositions to individual techniques and other baselines. The results underline the importance of finding an effective prompt composition. Our approach robustly ensures high detection performance, and is best in several settings. Moreover, first experiments on other tasks support its generalizability.}}, author = {{Spliethöver, Maximilian and Knebler, Tim and Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and Hüllermeier, Eyke and Wachsmuth, Henning}}, booktitle = {{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)}}, editor = {{Chiruzzo, Luis and Ritter, Alan and Wang, Lu}}, isbn = {{979-8-89176-189-6}}, pages = {{2421–2449}}, publisher = {{Association for Computational Linguistics}}, title = {{{Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection}}}, year = {{2025}}, } @article{61245, author = {{Barkhausen, Franziska and Ares Santos, Laura and Schumacher, Stefan and Sperling, Jan}}, issn = {{2469-9926}}, journal = {{Physical Review A}}, number = {{3}}, publisher = {{American Physical Society (APS)}}, title = {{{Entanglement between dependent degrees of freedom: Quasiparticle correlations}}}, doi = {{10.1103/physreva.111.032404}}, volume = {{111}}, year = {{2025}}, }