@article{51348,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>With 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.</jats:p>}},
  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{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}},
}

@article{51349,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Recent 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 (<jats:italic>N</jats:italic> = 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.</jats:p>}},
  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}},
}