Companies in every industry are striving to harness the potential of artificial intelligence (AI) to improve business processes through process automation or business optimization. However, the successful implementation of AI systems remains a challenge for individual companies. Reasons for this include the lack of diverse and high-quality data for training powerful AI models within single organizations, although the volume of interorganizational dispersed data is constantly growing (the so-called distributed data dilemma). In addition, there are increasing requirements for data privacy, security, transparence, and sustainability of AI-based solutions.
Interorganizational collaboration based on the give-and-take paradigm to jointly work on and improve AI-based solutions through trustworthy data collection and sharing or decentralized learning has the potential to overcome these challenges and realize AI ecosystems that are mutually beneficial for all stakeholders (value co-creation).
- Trustworthy data sharing in healthcare with Blockchain
- Trustworthy AI solutions in oncology
- Understanding health misinformation spread with the help of AI
- CO2 footprint prediction of construction projects (with REHAU New Ventures)
- Energy-aware AI development
- Cross-Silo Federated Learning in Enterprise Networks with Cooperative and Competing Actors