@techreport{ZerfassStieglitzClausenetal.2023,
  author    = {Zerfaß, Ansgar and Stieglitz, Stefan and Clausen, S{\"u}nje and Ziegele, Daniel and Berger, Karen},
  title     = {Communications trend radar 2023},
  series = {Communication insights},
  volume    = {17},
  journal   = {Communication insights},
  publisher = {Academic Society for Management \& Communication},
  address   = {Leipzig},
  issn      = {2749-893X},
  doi       = {10419/270993},
  pages     = {35},
  year      = {2023},
  abstract  = {How do social changes, new technologies or new management trends affect communication work? A team of researchers at Leipzig University and the University of Potsdam (Germany) observed new developments in related disciplines. As a result, the five most important trends for corporate communications are identified annually and published in the Communications Trend Radar. Thus, Communications managers can identify challenges and opportunities at an early stage, take a position, address issues and make decisions. For 2023, the Communications Trend Radar identifies five key trends for corporate communications: State Revival, Scarcity Management, Unimagination, Parallel Worlds, Augemented Workflows.},
  language  = {en}
}
@incollection{Grum2020,
  author    = {Grum, Marcus},
  title     = {Managing human and artificial knowledge bearers},
  series = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings},
  booktitle = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings},
  editor    = {Shishkov, Boris},
  publisher = {Springer International Publishing AG},
  address   = {Cham},
  isbn      = {978-3-030-52305-3},
  doi       = {10.1007/978-3-030-52306-0_12},
  pages     = {182 -- 201},
  year      = {2020},
  abstract  = {As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.},
  language  = {en}
}
@techreport{GagrčinSchaetzRakowskietal.2021,
  author    = {Gagrčin, Emilija and Schaetz, Nadja and Rakowski, Niklas and Toth, Roland and Renz, Andr{\´e} and Vladova, Gergana and Emmer, Martin},
  title     = {We and AI},
  publisher = {Weizenbaum Institute for the Networked Society - the German Internet},
  address   = {Berlin},
  doi       = {10.34669/wi/1},
  pages     = {70},
  year      = {2021},
  language  = {en}
}
@inproceedings{ClausenBruenkerStieglitz2023,
  author    = {Clausen, S{\"u}nje and Br{\"u}nker, Felix and Stieglitz, Stefan},
  title     = {Towards responsible augmentation},
  series = {ACIS 2023 proceedings},
  booktitle = {ACIS 2023 proceedings},
  publisher = {Australasian Association for Information Systems},
  address   = {Wellington},
  pages     = {9},
  year      = {2023},
  abstract  = {Artificial intelligence (AI)-based technologies can increasingly perform knowledge work tasks, such as medical diagnosis. Thereby, it is expected that humans will not be replaced by AI but work closely with AI-based technology ("augmentation"). Augmentation has ethical implications for humans (e.g., impact on autonomy, opportunities to flourish through work), thus, developers and managers of AI-based technology have a responsibility to anticipate and mitigate risks to human workers. However, doing so can be difficult as AI encompasses a wide range of technologies, some of which enable fundamentally new forms of interaction. In this research-in-progress paper, we propose the development of a taxonomy to categorize unique characteristics of AI-based technology that influence the interaction and have ethical implications for human workers. The completed taxonomy will support researchers in forming cumulative knowledge on the ethical implications of augmentation and assist practitioners in the ethical design and management of AI-based technology in knowledge work.},
  language  = {en}
}
@incollection{ThimGrumSchueffleretal.2021,
  author    = {Thim, Christof and Grum, Marcus and Sch{\"u}ffler, Arnulf and Roling, Wiebke and Kluge, Annette and Gronau, Norbert},
  title     = {A concept for a distributed Interchangeable knowledge base in CPPS},
  series = {Towards sustainable customization: cridging smart products and manufacturing systems},
  booktitle = {Towards sustainable customization: cridging smart products and manufacturing systems},
  editor    = {Andersen, Ann-Louise and Andersen, Rasmus and Brunoe, Thomas Ditlev and Larsen, Maria Stoettrup Schioenning and Nielsen, Kjeld and Napoleone, Alessia and Kjeldgaard, Stefan},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-90699-3},
  doi       = {10.1007/978-3-030-90700-6_35},
  pages     = {314 -- 321},
  year      = {2021},
  abstract  = {As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can "forget" their present specialization.},
  language  = {en}
}