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Further information

  • Project Coordination: Dr. Stephan Lingner; Julia Thelen, M. Sc.
  • EA-Team „DiA“: Stephan Lingner, Prof. Dr. Jan Cornelius Schmidt, Julia Thelen
  • Project Group Members: Dr.-Ing. Mark Azzam (DLR), Professor Dr. Dr. hc. Carl Friedrich Gethmann (Siegen University), Professor Dr.-Ing. Verena Nitsch (RWTH Aachen), Professor Dr. Indra Spiecker (Frankfurt University), Dr. Claudio Zettel (DLR Project Management Agency)
  • Associated Partners: Dr. Nils Goerke (Bonn University), Professor Dr. Ingrid Ott (Karlsruhe Institute of Technology KIT)
  • Duration: 01/18 -12/20
  • Funding: German Aerospace Center (DLR)

DiA: Digital worlds of work in research and development. New options and challenges for science

How will we and how do we want to work in research and development in future? What are the chances and challenges behind the increasing use of “intelligent” applications in scientific workplaces? In how far might self-learning systems – for instance in empirical sciences – be supportive? The EA project “DiA – Digital worlds of work in research and development. New options and challenges for science” addresses these and further questions.

The digitisation of working environments particularly reaches all areas of publicly or privately funded research and development. New developments in the field of artificial intelligence (AI) now lead us to anticipate a quantum leap forward towards the future of basic and application-oriented research: systems pertinent to the concept mimic the network architectures of the human brain and facilitate their immanent “learning ability”. They are capable of making “experience-driven”, independent adjustments to their inventory of rewritable algorithms, independent of human programmers. Characterised as cognitive computing or machine learning, these capabilities of technical “intelligence” facilitate the use of these systems even for demanding tasks that were previously reserved for scientists. However, the inherent learning ability and flexibility of these systems also means that their internal processes remain hidden – in contrast to classical modelling, which may be complex but is rule-based and as such can still be cognitively accessed by experts.

For now, we have neither rudimentarily understood nor rationally assessed the perspectives and consequences of the use of adaptive systems and their interaction with their analogue “colleagues” in science. From a technical point of view, we need to ask what development perspectives, milestones, and areas of application of AI in research and development are realistically foreseeable. For the sake of the quality and validity of scientific work, we need to clarify whether the use of AI might lead to more efficient and to henceforth excellent research and development or whether it incurs deficits in the explicability and epistemic robustness of AI-supported research. Against this background, researchers may see their scientific autonomy and prerogative of interpretation at stake. From the point of view of work science, new challenges arise particularly for the development of future concepts guaranteeing critical competencies for new generations of researchers. Finally, it will be necessary to investigate what measures are necessary for the appropriate integration of self-learning systems into everyday research in order to preserve standards best practice in science.

The project team will therefore investigate and assess self-learning systems in science across disciplines. The project aims to gain meaningful insights into the desirable design of research and science management in a working world enriched with AI. The findings of this project are intended to inform research institutions in science as well as institutions concerned with science management and research policy. Furthermore, we are also going to formulate sound and targeted proposals for action. Profound recommendations for the operational level will be put forward.

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