Research
We study the mathematical foundations and practical deployment of trustworthy AI, with a focus on
federated learning (FL), explainability, robustness, and governance.
This project develops rigorous principles for trustworthy federated learning that go beyond accuracy to
incorporate fairness, interpretability, robustness, and accountability. We build theory and methods
for human-centric FL systems that protect agency and fundamental rights while enabling personalized models.
- Foundations for trustworthy FL (regularization, constraints, and risk trade-offs).
- Personalized explainability metrics aligned with user notions of similarity.
- Design criteria that connect mathematical guarantees to real-world deployment.
FLAIG — Forward-Looking AI Governance in Banking & Insurance
Funding: Business Finland (Co-research). Partners: University of Turku, Aalto University,
Tietoevry, OP Financial Group, Veritas.
FLAIG develops sector-specific AI governance frameworks, auditing principles, and
personalized explainability solutions for high-risk finance applications in the era of generative AI.
The project advances compliance with the EU AI Act and operationalizes fairness, transparency, and accountability
for real-world decision systems.
For a complete list of publications, see our
Google Scholar profile ↗.