@inproceedings{46289, author = {{Banh, Ngoc Chi and Scharlau, Ingrid and Rohlfing, Katharina J.}}, booktitle = {{52. Kongress der Deutschen Gesellschaft für Psychologie}}, editor = {{Bermeitinger, Christina and Greve, Werner }}, location = {{Hildesheim, Germany}}, title = {{{Folgen wiederholter Negation auf die Aufmerksamkeit}}}, year = {{2022}}, } @inproceedings{51343, abstract = {{This paper presents preliminary work on the formalization of three prominent cognitive biases in the diagnostic reasoning process over epileptic seizures, psychogenic seizures and syncopes. Diagnostic reasoning is understood as iterative exploration of medical evidence. This exploration is represented as a partially observable Markov decision process where the state (i.e., the correct diagnosis) is uncertain. Observation likelihoods and belief updates are computed using a Bayesian network which defines the interrelation between medical risk factors, diagnoses and potential findings. The decision problem is solved via partially observable upper confidence bounds for trees in Monte-Carlo planning. We compute a biased diagnostic exploration policy by altering the generated state transition, observation and reward during look ahead simulations. The resulting diagnostic policies reproduce reasoning errors which have only been described informally in the medical literature. We plan to use this formal representation in the future to inversely detect and classify biased reasoning in actual diagnostic trajectories obtained from physicians.}}, author = {{Battefeld, Dominik and Kopp, Stefan}}, booktitle = {{Proceedings of the 8th Workshop on Formal and Cognitive Reasoning}}, keywords = {{Diagnostic reasoning, Cognitive bias, Cognitive model, POMDP, Bayesian network, Epilepsy, CDSS}}, location = {{Trier}}, title = {{{Formalizing cognitive biases in medical diagnostic reasoning}}}, 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}}, } @inproceedings{61303, author = {{Artelt, André and Brinkrolf, Johannes and Visser, Roel and Hammer, Barbara}}, booktitle = {{Proceedings of the 14th International Joint Conference on Computational Intelligence}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Explaining Reject Options of Learning Vector Quantization Classifiers}}}, doi = {{10.5220/0011389600003332}}, year = {{2022}}, } @inproceedings{61302, author = {{Artelt, André and Visser, Roel and Hammer, Barbara}}, booktitle = {{ESANN 2022 proceedings}}, publisher = {{Ciaco - i6doc.com}}, title = {{{Model Agnostic Local Explanations of Reject}}}, doi = {{10.14428/esann/2022.es2022-34}}, year = {{2022}}, } @article{51349, abstract = {{AbstractRecent approaches to Explainable AI (XAI) promise to satisfy diverse user expectations by allowing them to steer the interaction in order to elicit content relevant to them. However, little is known about how and to what extent the explainee takes part actively in the process of explaining. To tackle this empirical gap, we exploratively examined naturally occurring everyday explanations in doctor–patient interactions (N = 11). Following the social design of XAI, we view explanations as emerging in interactions: first, we identified the verbal behavior of both the explainer and the explainee in the sequential context, which we could assign to phases that were either monological or dialogical; second, we investigated in particular who was responsible for the initiation of the different phases. Finally, we took a closer look at the global conversational structure of explanations by applying a context-sensitive model of organizational jobs, thus adding a third layer of analysis. Results show that in our small sample of conversational explanations, both monological and dialogical phases varied in their length, timing of occurrence (at the early or later stages of the interaction) and their initiation (by the explainer or the explainee). They alternated several times in the course of the interaction. However, we also found some patterns suggesting that all interactions started with a monological phase initiated by the explainer. Both conversational partners contributed to the core organizational job that constitutes an explanation. We interpret the results as an indication for naturally occurring everyday explanations in doctor–patient interactions to be co-constructed on three levels of linguistic description: (1) by switching back and forth between monological to dialogical phases that (2) can be initiated by both partners and (3) by the mutual accomplishment and thus responsibility for an explanation’s core job that is crucial for the success of the explanation. Because of the explorative nature of our study, these results need to be investigated (a) with a larger sample and (b) in other contexts. However, our results suggest that future designs of artificial explainable systems should design the explanatory dialogue in such a way that it includes monological and dialogical phases that can be initiated not only by the explainer but also by the explainee, as both contribute to the core job of explicating procedural, clausal, or conceptual relations in explanations.}}, author = {{Fisher, Josephine Beryl and Lohmer, Vivien and Kern, Friederike and Barthlen, Winfried and Gaus, Sebastian and Rohlfing, Katharina}}, issn = {{0933-1875}}, journal = {{KI - Künstliche Intelligenz}}, keywords = {{Artificial Intelligence}}, number = {{3-4}}, pages = {{317--326}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Exploring monological and dialogical phases in naturally occurring explanations}}}, doi = {{10.1007/s13218-022-00787-1}}, volume = {{36}}, year = {{2022}}, } @inproceedings{32247, author = {{Alshomary, Milad and Rieskamp, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 9th International Conference on Computational Models of Argument}}, pages = {{21 -- 31}}, title = {{{Generating Contrastive Snippets for Argument Search}}}, doi = {{http://dx.doi.org/10.3233/FAIA220138}}, year = {{2022}}, } @inproceedings{30840, author = {{Alshomary, Milad and El Baff, Roxanne and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics}}, pages = {{8782 -- 8797}}, title = {{{The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments}}}, year = {{2022}}, }