Human–AI Decision-Making and Data Sharing
We examine how human-AI collaboration can be designed to strengthen rather than undermine human judgment.
AI is transforming how decisions are made across personal and professional contexts. We investigate when and why people turn to AI for advice, how they engage with and rely on its recommendations, and what shapes their perceptions of fairness and transparency. Our research further explores the (meta)cognitive mechanisms and social dynamics of human–AI interaction, the factors that foster or hinder data sharing, and the development of tools to capture reluctance toward AI. Together, these projects aim to chart pathways for responsible human–AI collaboration—unlocking the benefits of AI while safeguarding human agency and critical thinking.
Current projects
- Human meta-cognition in AI-assisted decision-making
- Data sharing with AI vs. with humans
- Persuasive strategies for Duolingo data sharing
- Measuring human reluctance toward algorithmic decision-making
Completed projects
- Adoption of algorithmic advice in human resources
- Ochmann, J., Zilker, S., Michels, L., Laumer, S., Tiefenbeck, V., Matzner, M. (2025), The effects of information and incentive interventions on the adoption of algorithms in human resources: An experimental study. Accepted in The DATA BASE for Advances in Information Systems, 56(1), 44-60.
- Ochmann, J., Michels, L., Tiefenbeck, V., Maier, C., Laumer, S. (2024), Perceived algorithmic fairness: An empirical study of transparency and anthropomorphism in algorithmic recruiting. Information Systems Journal, doi.org/10.1111/isj.12482.
- Schneider, L., Kharlamova, M., (2025), Mind Over Machine: Navigating Human Metacognition When Using Generative AI. ICIS 2025 Proceedings, Nashville, USA.