@article{53228, author = {{Tirkolaee, Erfan Babaee and Torkayesh, Ali Ebadi and Tavana, Madjid and Goli, Alireza and Simic, Vladimir and Ding, Weiping}}, issn = {{0952-1976}}, journal = {{Engineering Applications of Artificial Intelligence}}, keywords = {{Electrical and Electronic Engineering, Artificial Intelligence, Control and Systems Engineering}}, publisher = {{Elsevier BV}}, title = {{{An integrated decision support framework for resilient vaccine supply chain network design}}}, doi = {{10.1016/j.engappai.2023.106945}}, volume = {{126}}, year = {{2023}}, } @article{53230, author = {{Mahdiraji, Hannan Amoozad and Tavana, Madjid and Rezayar, Ali}}, issn = {{0196-9722}}, journal = {{Cybernetics and Systems}}, keywords = {{Artificial Intelligence, Information Systems, Software}}, number = {{1}}, pages = {{104--137}}, publisher = {{Informa UK Limited}}, title = {{{A Game-Theoretic Framework for Analyzing the Impact of Social Responsibility and Supply Chain Profitability}}}, doi = {{10.1080/01969722.2022.2055402}}, volume = {{54}}, year = {{2023}}, } @article{53356, author = {{Terhörst, Philipp and Huber, Marco and Damer, Naser and Kirchbuchner, Florian and Raja, Kiran and Kuijper, Arjan}}, issn = {{2637-6407}}, journal = {{IEEE Transactions on Biometrics, Behavior, and Identity Science}}, keywords = {{Artificial Intelligence, Computer Science Applications, Computer Vision and Pattern Recognition, Instrumentation}}, number = {{2}}, pages = {{288--297}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Pixel-Level Face Image Quality Assessment for Explainable Face Recognition}}}, doi = {{10.1109/tbiom.2023.3263186}}, volume = {{5}}, year = {{2023}}, } @article{50262, abstract = {{AbstractExplainable 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.}}, 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}}, } @article{48678, abstract = {{Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts—measured as breaks in the finger movement data before the correct gesture was carried out—when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.}}, author = {{Groß, André and Singh, Amit and Banh, Ngoc Chi and Richter, Birte and Scharlau, Ingrid and Rohlfing, Katharina J. and Wrede, Britta}}, issn = {{2296-9144}}, journal = {{Frontiers in Robotics and AI}}, keywords = {{Artificial Intelligence, Computer Science Applications}}, publisher = {{Frontiers Media SA}}, title = {{{Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue}}}, doi = {{10.3389/frobt.2023.1236184}}, volume = {{10}}, year = {{2023}}, } @inproceedings{27506, abstract = {{Explainability for machine learning gets more and more important in high-stakes decisions like real estate appraisal. While traditional hedonic house pricing models are fed with hard information based on housing attributes, recently also soft information has been incorporated to increase the predictive performance. This soft information can be extracted from image data by complex models like Convolutional Neural Networks (CNNs). However, these are intransparent which excludes their use for high-stakes financial decisions. To overcome this limitation, we examine if a two-stage modeling approach can provide explainability. We combine visual interpretability by Regression Activation Maps (RAM) for the CNN and a linear regression for the overall prediction. Our experiments are based on 62.000 family homes in Philadelphia and the results indicate that the CNN learns aspects related to vegetation and quality aspects of the house from exterior images, improving the predictive accuracy of real estate appraisal by up to 5.4%.}}, author = {{Kucklick, Jan-Peter}}, booktitle = {{55th Annual Hawaii International Conference on System Sciences (HICSS-55)}}, keywords = {{Explainable Artificial Intelligence (XAI), Regression Activation Maps, Real Estate Appraisal, Convolutional Block Attention Module, Computer Vision}}, location = {{Virtual}}, title = {{{Visual Interpretability of Image-based Real Estate Appraisal}}}, year = {{2022}}, } @inproceedings{29539, abstract = {{Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.}}, author = {{Kucklick, Jan-Peter}}, booktitle = {{Wirtschaftsinformatik 2022 Proceedings}}, keywords = {{Explainable Artificial Intelligence, XAI, Interpretability, Decision Support Systems, Taxonomy}}, location = {{Nürnberg (online)}}, title = {{{Towards a model- and data-focused taxonomy of XAI systems}}}, year = {{2022}}, } @article{30193, abstract = {{The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.}}, author = {{Meyer, Maurice and Fichtler, Timm and Koldewey, Christian and Dumitrescu, Roman}}, issn = {{0890-0604}}, journal = {{Artificial Intelligence for Engineering Design, Analysis and Manufacturing}}, keywords = {{Artificial Intelligence, Industrial and Manufacturing Engineering}}, publisher = {{Cambridge University Press (CUP)}}, title = {{{Potentials and challenges of analyzing use phase data in product planning of manufacturing companies}}}, doi = {{10.1017/s0890060421000408}}, volume = {{36}}, year = {{2022}}, } @article{34046, author = {{Hoffmann, Christin and Thommes, Kirsten}}, issn = {{2168-2291}}, journal = {{IEEE Transactions on Human-Machine Systems}}, keywords = {{Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, Human-Computer Interaction, Signal Processing, Control and Systems Engineering, Human Factors and Ergonomics}}, pages = {{1--11}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Seizing the Opportunity for Automation—How Traffic Density Determines Truck Drivers' Use of Cruise Control}}}, doi = {{10.1109/thms.2022.3212335}}, year = {{2022}}, } @article{44637, author = {{Hoppe, Julia Amelie and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}}, issn = {{2451-9588}}, journal = {{Computers in Human Behavior Reports}}, keywords = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}}, doi = {{10.1016/j.chbr.2022.100258}}, volume = {{9}}, year = {{2022}}, } @article{34295, author = {{Hoppe, Julia Amelie and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}}, issn = {{2451-9588}}, journal = {{Computers in Human Behavior Reports}}, keywords = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}}, doi = {{10.1016/j.chbr.2022.100258}}, year = {{2022}}, } @article{44636, author = {{Hoppe, Julia A. and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}}, issn = {{2451-9588}}, journal = {{Computers in Human Behavior Reports}}, keywords = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}}, doi = {{10.1016/j.chbr.2022.100258}}, volume = {{9}}, year = {{2022}}, } @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}}, } @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{52862, author = {{Turhan, Anni-Yasmin}}, issn = {{0933-1875}}, journal = {{KI - Künstliche Intelligenz}}, keywords = {{Artificial Intelligence}}, number = {{1}}, pages = {{1--4}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{A Double Take at Conferences: The Hybrid Format}}}, doi = {{10.1007/s13218-022-00758-6}}, volume = {{36}}, year = {{2022}}, } @article{53236, author = {{Tavana, Madjid and Shaabani, Akram and Di Caprio, Debora and Bonyani, Abbas}}, issn = {{0957-4174}}, journal = {{Expert Systems with Applications}}, keywords = {{Artificial Intelligence, Computer Science Applications, General Engineering}}, publisher = {{Elsevier BV}}, title = {{{A novel Interval Type-2 Fuzzy best-worst method and combined compromise solution for evaluating eco-friendly packaging alternatives}}}, doi = {{10.1016/j.eswa.2022.117188}}, volume = {{200}}, year = {{2022}}, } @article{45849, abstract = {{AbstractDependence Logic was introduced by Jouko Väänänen in 2007. We study a propositional variant of this logic(PDL)and investigate a variety of parameterisations with respect to central decision problems. The model checking problem (MC) ofPDLisNP-complete (Ebbing and Lohmann, SOFSEM 2012). The subject of this research is to identify a list of parameterisations (formula-size, formula-depth, treewidth, team-size, number of variables) under which MC becomes fixed-parameter tractable. Furthermore, we show that the number of disjunctions or the arity of dependence atoms (dep-arity) as a parameter both yield a paraNP-completeness result. Then, we consider the satisfiability problem (SAT) which classically is known to beNP-complete as well (Lohmann and Vollmer, Studia Logica 2013). There we are presenting a different picture: under team-size, or dep-arity SAT isparaNP-complete whereas under all other mentioned parameters the problem isFPT. Finally, we introduce a variant of the satisfiability problem, asking for a team of a given size, and show for this problem an almost complete picture.}}, author = {{Mahmood, Yasir and Meier, Arne}}, issn = {{1012-2443}}, journal = {{Annals of Mathematics and Artificial Intelligence}}, keywords = {{Applied Mathematics, Artificial Intelligence}}, number = {{2-3}}, pages = {{271--296}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Parameterised complexity of model checking and satisfiability in propositional dependence logic}}}, doi = {{10.1007/s10472-021-09730-w}}, volume = {{90}}, year = {{2022}}, } @article{33684, author = {{Schade, Robert and Kenter, Tobias and Elgabarty, Hossam and Lass, Michael and Schütt, Ole and Lazzaro, Alfio and Pabst, Hans and Mohr, Stephan and Hutter, Jürg and Kühne, Thomas and Plessl, Christian}}, issn = {{0167-8191}}, journal = {{Parallel Computing}}, keywords = {{Artificial Intelligence, Computer Graphics and Computer-Aided Design, Computer Networks and Communications, Hardware and Architecture, Theoretical Computer Science, Software}}, publisher = {{Elsevier BV}}, title = {{{Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms}}}, doi = {{10.1016/j.parco.2022.102920}}, volume = {{111}}, year = {{2022}}, } @article{48780, abstract = {{Explainable Artificial Intelligence (XAI) has mainly focused on static learning tasks so far. In this paper, we consider XAI in the context of online learning in dynamic environments, such as learning from real-time data streams, where models are learned incrementally and continuously adapted over the course of time. More specifically, we motivate the problem of explaining model change, i.e. explaining the difference between models before and after adaptation, instead of the models themselves. In this regard, we provide the first efficient model-agnostic approach to dynamically detecting, quantifying, and explaining significant model changes. Our approach is based on an adaptation of the well-known Permutation Feature Importance (PFI) measure. It includes two hyperparameters that control the sensitivity and directly influence explanation frequency, so that a human user can adjust the method to individual requirements and application needs. We assess and validate our method’s efficacy on illustrative synthetic data streams with three popular model classes.}}, author = {{Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke}}, issn = {{0933-1875}}, journal = {{KI - Künstliche Intelligenz}}, keywords = {{Artificial Intelligence}}, number = {{3-4}}, pages = {{211--224}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Agnostic Explanation of Model Change based on Feature Importance}}}, doi = {{10.1007/s13218-022-00766-6}}, volume = {{36}}, year = {{2022}}, }