@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}},
}

@inproceedings{23779,
  abstract     = {{Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. 
Im Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden.}},
  author       = {{Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Panzner, Melina and Soltenborn, Christian}},
  booktitle    = {{Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}},
  keywords     = {{Artificial Intelligence Product Creation Literature Review}},
  location     = {{Montreal, Kanada}},
  title        = {{{A Meta-Review on Artificial Intelligence in Product Creation}}},
  year         = {{2021}},
}

@article{37155,
  abstract     = {{Artificial intelligence (AI) has moved beyond the planning phase in many organisations and it is often accompanied by uncertainties and fears of job loss among employees. It is crucial to manage employees{\textquoteright} attitudes towards the deployment of an AI-based technology effectively and counteract possible resistance behaviour. We present lessons learned from an industry case where we conducted interviews with affected employees. We evaluated our results with managers across industries and found that that the deployment of AI-based technologies does not differ from other IT, but that the change is perceived differently due to misguided expectations. }},
  author       = {{Stieglitz, Stefan and Möllmann (Frick), Nicholas R. J. and Mirbabaie, Milad and Hofeditz, Lennart and Ross, Björn}},
  issn         = {{1477-9064}},
  journal      = {{International Journal of Management Practice}},
  keywords     = {{Artificial Intelligence, Change Management, Resistance, AI-Driven Change, AI Deployment, AI Perception}},
  publisher    = {{Inderscience}},
  title        = {{{Recommendations for Managing AI-Driven Change Processes: When Expectations Meet Reality}}},
  year         = {{2021}},
}

@article{30114,
  author       = {{Gölz, Christian Johannes and Mora, K. and Rudisch, J. and Gaidai, Roman and Reuter, E. and Godde, B. and Reinsberger, Claus and Voelcker-Rehage, C. and Vieluf, S.}},
  issn         = {{0893-6080}},
  journal      = {{Neural Networks}},
  keywords     = {{Artificial Intelligence, Cognitive Neuroscience}},
  pages        = {{363--374}},
  publisher    = {{Elsevier BV}},
  title        = {{{Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns}}},
  doi          = {{10.1016/j.neunet.2021.04.029}},
  volume       = {{142}},
  year         = {{2021}},
}

@article{31400,
  author       = {{Goelz, C. and Mora, K. and Rudisch, J. and Gaidai, R. and Reuter, E. and Godde, B. and Reinsberger, Claus and Voelcker-Rehage, C. and Vieluf, S.}},
  issn         = {{0893-6080}},
  journal      = {{Neural Networks}},
  keywords     = {{Artificial Intelligence, Cognitive Neuroscience}},
  pages        = {{363--374}},
  publisher    = {{Elsevier BV}},
  title        = {{{Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns}}},
  doi          = {{10.1016/j.neunet.2021.04.029}},
  volume       = {{142}},
  year         = {{2021}},
}

@inproceedings{27491,
  abstract     = {{ Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.}},
  author       = {{Höper, Lukas}},
  booktitle    = {{21st Koli Calling International Conference on Computing Education Research}},
  isbn         = {{9781450384889}},
  keywords     = {{data awareness, machine learning, data science education, data-driven digital artifacts, artificial intelligence}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Developing and Evaluating the Concept Data Awareness for K12 Computing Education}}},
  doi          = {{10.1145/3488042.3490509}},
  year         = {{2021}},
}

@inproceedings{15332,
  abstract     = {{Artificial intelligence (AI) has the potential for far-reaching – in our opinion – irreversible changes.
They range from effects on the individual and society to new societal and social issues. The question arises
as to how students can learn the basic functioning of AI systems, what areas of life and society are affected
by these and – most important – how their own lives are affected by these changes. Therefore, we are developing and evaluating school materials for the German ”Science Year AI”. It can be used for students of all
school types from the seventh grade upwards and will be distributed to about 2000 schools in autumn with
the support of the Federal Ministry of Education and Research. The material deals with the following aspects
of AI: Discussing everyday experiences with AI, how does machine learning work, historical development
of AI concepts, difference between man and machine, future distribution of roles between man and machine,
in which AI world do we want to live and how much AI would we like to have in our lives. Through an
accompanying evaluation, high quality of the technical content and didactic preparation is achieved in order
to guarantee the long-term applicability in the teaching context in the different age groups and school types.
In this paper, we describe the current state of the material development, the challenges arising, and the results
of tests with different classes to date. We also present first ideas for evaluating the results.}},
  author       = {{Schlichtig, Michael and Opel, Simone Anna and Budde, Lea and Schulte, Carsten}},
  booktitle    = {{ISSEP 2019 - 12th International conference on informatics in schools: Situation, evaluation and perspectives, Local Proceedings}},
  editor       = {{Jasutė, Eglė and Pozdniakov, Sergei}},
  isbn         = {{978-9925-553-27-3}},
  keywords     = {{Artificial Intelligence, Machine Learning, Teaching Material, Societal Aspects, Ethics. Social Aspects, Science Year, Simulation Game}},
  location     = {{Lanarca}},
  pages        = {{65 -- 73}},
  title        = {{{Understanding Artificial Intelligence – A Project for the Development of Comprehensive Teaching Material}}},
  volume       = {{12}},
  year         = {{2019}},
}

@article{42677,
  author       = {{Klowait, Nils}},
  issn         = {{0951-5666}},
  journal      = {{AI & SOCIETY}},
  keywords     = {{Artificial Intelligence, Human-Computer Interaction, Philosophy}},
  number       = {{4}},
  pages        = {{527--536}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{The quest for appropriate models of human-likeness: anthropomorphism in media equation research}}},
  doi          = {{10.1007/s00146-017-0746-z}},
  volume       = {{33}},
  year         = {{2017}},
}

@article{48306,
  abstract     = {{<jats:p>The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.</jats:p>}},
  author       = {{Habernal, Ivan and Gurevych, Iryna}},
  issn         = {{0891-2017}},
  journal      = {{Computational Linguistics}},
  keywords     = {{Artificial Intelligence, Computer Science Applications, Linguistics and Language, Language and Linguistics}},
  number       = {{1}},
  pages        = {{125--179}},
  publisher    = {{MIT Press}},
  title        = {{{Argumentation Mining in User-Generated Web Discourse}}},
  doi          = {{10.1162/coli_a_00276}},
  volume       = {{43}},
  year         = {{2016}},
}

@article{52747,
  author       = {{Borgwardt, Stefan and Mailis, Theofilos and Peñaloza, Rafael and Turhan, Anni-Yasmin}},
  issn         = {{1861-2032}},
  journal      = {{Journal on Data Semantics}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Information Systems}},
  number       = {{2}},
  pages        = {{55--75}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Answering Fuzzy Conjunctive Queries Over Finitely Valued Fuzzy Ontologies}}},
  doi          = {{10.1007/s13740-015-0055-y}},
  volume       = {{5}},
  year         = {{2016}},
}

@article{52803,
  author       = {{Borgwardt, Stefan and Mailis, Theofilos and Peñaloza, Rafael and Turhan, Anni-Yasmin}},
  issn         = {{1861-2032}},
  journal      = {{Journal on Data Semantics}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Information Systems}},
  number       = {{2}},
  pages        = {{55--75}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Answering Fuzzy Conjunctive Queries Over Finitely Valued Fuzzy Ontologies}}},
  doi          = {{10.1007/s13740-015-0055-y}},
  volume       = {{5}},
  year         = {{2016}},
}

@article{50438,
  author       = {{Baraté, Adriano and Haus, Goffredo and Ludovico, Luca Andrea and Mauro, Davide Andrea}},
  issn         = {{1796-2048}},
  journal      = {{Journal of Multimedia}},
  keywords     = {{Electrical and Electronic Engineering, Artificial Intelligence, Media Technology}},
  number       = {{2}},
  publisher    = {{Academy Publisher}},
  title        = {{{IEEE 1599 for Live Musical and Theatrical Performances}}},
  doi          = {{10.4304/jmm.7.2.170-178}},
  volume       = {{7}},
  year         = {{2012}},
}

@inproceedings{9736,
  abstract     = {{Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve the dependability of mechatronic systems. This paper introduces a multi-level dependability concept for self-optimizing mechatronic systems and shows how planning can be used to improve the availability and reliability of systems in the operating stages.}},
  author       = {{Klöpper, Benjamin and Sondermann-Wölke, Christoph and Romaus, Christoph and Vöcking, Henner}},
  booktitle    = {{Computational Intelligence in Control and Automation, 2009. CICA 2009. IEEE Symposium on}},
  keywords     = {{multilevel dependability concept, probabilistic planning, self-optimizing mechatronic systems, systems reliability, mechatronics, planning (artificial intelligence), self-adjusting systems}},
  pages        = {{104 --111}},
  title        = {{{Probabilistic planning integrated in a multi-level dependability concept for mechatronic systems}}},
  doi          = {{10.1109/CICA.2009.4982790}},
  year         = {{2009}},
}