Artificial intelligence is transforming how decisions are made, information is communicated, and value is created. For this development to realize its full potential, AI must be understood, designed responsibly, and meaningfully embedded in organizations and society.
We examine artificial intelligence as a socio-technical phenomenon across three levels:
- How does AI influence individual decisions, perceptions, and identity processes?
- How do technical architectures and platform logics shape information environments and societal power structures?
- Which organizational and institutional conditions are necessary to ensure that AI can be used legitimately and responsibly over the long term?
At the center of our work lies the interplay between humans, AI, and organizations.
Our research follows three overarching principles:
- Understanding AI – How does AI influence individual decisions, perceptions, and identity?
- Shaping AI – How can AI systems be designed responsibly from technical, economic, and normative perspectives?
- Embedding AI – How can AI be sustainably and legitimately embedded in organizations and society?
These three perspectives structure our research activities, projects, and knowledge transfer initiatives.
Understanding AI: Individual Decisions, Perceptions, and Identity
Analyzing Human–AI Interaction and Individual Decision-Making Processes
AI systems increasingly support or automate human decision-making. Particularly in high-stakes, time-critical, or emotionally charged situations, tensions emerge between algorithmic precision, subjective perceptions, and individual responsibility. AI influences not only the quality of decisions but also self-perception, learning behavior, and attribution processes. In this area, we analyze the cognitive and situational conditions of human–AI interaction and their consequences for individual behavior.
In this area, we investigate:
- how people perceive, evaluate, and integrate algorithmic recommendations into their decision-making processes,
- under which conditions trust, acceptance, or algorithmic skepticism emerge and are lost again,
- how AI changes attributions of responsibility, success attributions, and self-efficacy,
- how AI influences identity formation and learning behavior, for example through the use of generative AI in education and knowledge work.
A particular focus lies on realistically modelling decision-making processes involving AI and deriving approaches for a reflective and responsible use of AI at the individual level.
Shaping AI: Systems, Platform Logics, and Structural Effects
Designing the Socio-Technical Architecture of AI
AI is not a neutral actor. Technical design decisions, data infrastructures, and platform logics determine which information becomes visible, whose perspectives are privileged, and which groups are structurally disadvantaged. AI-based systems therefore influence not only individual decisions but also shape information environments, market structures, and societal power relations. In this area, we investigate the technical, economic, and normative conditions for the responsible design of AI.
In this area, we investigate:
- how AI-based platforms create information-limiting environments (e.g., filter bubbles) through algorithmic personalization and how these effects can be reduced,
- how fairness, transparency, and explainability in AI systems can be technically implemented and made verifiable,
- how bias, discrimination, and structural disadvantage (e.g., in relation to gender or other marginalized groups) emerge and how they can be reduced,
- how AI-based platform economies give rise to new power structures, dependencies, and forms of privatization of the digital public sphere.
A particular focus lies on developing concrete design approaches that combine technical performance with societal responsibility and a critical reflection on the effects of economic power.
Embedding AI: Governance, Organizations, and Societal Responsibility
Developing Institutional and Strategic Conditions for AI
For AI to be used legitimately and gain societal acceptance over the long term, it must be embedded within appropriate organizational and institutional structures. Responsibility does not arise through technical design alone, but through clear responsibilities, processes, control mechanisms, and strategic objectives. Organizations do not operate in isolation but within value networks, platform ecosystems, and regulatory frameworks.
In this area, we investigate:
- how organizational AI governance can be established, implemented, and continuously developed,
- which roles, competencies, and resources are required for the development, monitoring, and responsible use of AI, and how users can be enabled to critically reflect on AI systems,
- how organizations develop and govern AI in collaboration with partners and within innovation and platform ecosystems,
- how AI can be deployed in alignment with societal goals, for example in the areas of human rights, digital sovereignty, participation, and sustainability.
A particular focus lies on developing strategic governance models that systematically consider and actively shape the long-term economic and societal impacts of AI.