Ongoing collaborations:
KI-Akademie OWL
The AI Academy OWL conducts research on how artificial intelligence can be used responsibly, efficiently, and sustainably. Its focus is on identifying the risks associated with highly complex AI models and developing specialized, resource-efficient AI solutions. In doing so, the academy concentrates particularly on two highly relevant areas: the safety of AI processes and the development of AI methods for applications with limited data, such as in the context of inclusion. Through interdisciplinary research, we bring together technology, the social sciences, and the humanities to make AI understandable, safe, and inclusive—for businesses, educational institutions, and society.
SAIL: Sustainable Life-Cycle of Intelligent Socio-Technical Systems, 2022-2026
SAIL is an interdisciplinary and interinstitutional collaboration of Bielefeld University, Paderborn University, Bielefeld University of Applied Sciences, and OWL University of Applied Sciences and Arts, funded by the MKW NRW. Current systems that incorporate AI technology mainly target the introduction phase, where a core component is training and adaptation of AI models based on given example data. SAIL’s focus on the full life-cycle moves the current emphasis towards sustainable long-term development in real life. The joint project SAIL addresses both basic research in the field of AI, its implications from the perspective of the humanities and social sciences, and concrete applications in the field of Industry 4.0 and Intelligent Healthcare.
TRR 318: Constructing Explainability, 2021-2029
The Collaborative Research Center (TRR 318) „Constructing Explainability“, funded by the DFG at the universities of Bielefeld and Paderborn, addresses the question of how to make algorithmic decisions transparent. Decisions by black-box methods of modern artificial intelligence are especially considered. The central hypothesis is that explanations are most effective if they are co-constructed by explainer and explainee. The mechanisms of co-construction will be investigated by an interdisciplinary consortium to lay the foundations for new paradigms in human-computer interaction to that humans are empowered to make sovereign and informed decisions when interacting with intelligent systems. The TRR is structured in three research areas: A “Explaining”, B “Social practice”, C “Representing and computing explanations”. The research areas consist of interdisciplinary subprojects in which 21 Principal Investigators from Linguistics, Psychology, Media Science, Sociology, Economics and Computer Science from both universities are involved.
Research training group Dataninja, 2021-2025
The goal of trustworthy AI is to offer intelligent methods and agents that produce adaptive behavior in real world scenarios, and are transparent in their decision making as they are able to justify and explain their decisions. The Dataninja research training group is focused on developing novel methods in this area of trustworthy Artificial Intelligence. The goal is to make AI more robust, easier to integrate, more trustworthy, and more secure. Dataninja connects leading research groups in AI in North Rhine-Westphalia and offers young academics an ideal opportunity to establish their own research in the core areas of AI as part of an excellent network.
Competence center Arbeitswelt.Plus - KI in der Arbeitswelt des Industriellen Mittelstandes in OstWestfalenLippe (KIAM), 2020-2025
How will Artificial Intelligence (AI) change the world of work? And how can companies use new technologies? The identification of possible uses for AI and the development of specific solutions pose challenges for small and medium-sized enterprises (SMEs) in particular: a shortage of skilled workers, a lack of technological prerequisites or unclear organizational structures are hurdles that have to be overcome on the way to the introduction of AI-based processes. Above all, there is a lack of work research close to small and medium-sized companies that provides solution and application knowledge about AI in order to relieve companies of the uncertainty before the introduction. These tasks are to be taken over by the Arbeitsswelt.Plus competence center.
RailCampus OWL
Tomorrow’s mobility is a key factor for the future viability of regions. Transport by rail offers particularly great potential for automation - be it in equipping the wagons, loading and unloading freight trains or running operations. In order to strengthen the Deutsche Bahn in both passenger and freight transport, these opportunities for the application of new technologies must also be used: With the RailCampus OWL, a focal point for these future tasks with high visibility throughout Germany is to be created - as a place of research, development and testing, as well as a campus for studies and further education. The focus of research at the RailCampus OWL will be application-oriented, so that approaches can be tried out directly on site and checked for operability. A group of partners of Campus OWL with diverse scientific resources, DB Systemtechnik with its internationally unique test rigs and test tracks, and DB Cargo AG with internationally recognized know-how in the field of rail technology and operation strengthen the RailCampus OWL. A growing number of medium-sized companies and other partners of the it’s OWL network ensure the transfer to business and society.
it’s OWL: Technology transfer
The successful technology transfer to medium-sized companies is a unique selling point of it’s OWL. Small and medium-sized companies can use expertise, methods and technologies from the cluster in transfer projects with a university or research institution to solve specific challenges of digital transformation. The projects are easy to apply for and can be implemented quickly. Their effects are directly visible in the company. In this way, medium-sized companies in particular can take important steps on the way to Industry 4.0. The transfer projects make a key contribution to the digitization of processes, products and services. This includes, for example, the intelligent networking and self-optimization of machines and systems, IT security, the design of human-machine interfaces, efficient energy management or new business models.