trustworthy-ai

Seminar on trustworthy artificial intelligence from a sociotechnical perspective, covering high-impact decisions, social networks, and human-AI collaboration.

Round table on the role of artificial intelligence in science and health, with a focus on AI4Health, responsible research, and trustworthy deployment.

Masterclass on algorithmic discrimination, bias, and trustworthy AI in public-sector decision-making with a gender perspective.

Outreach talk introducing high-school students to artificial intelligence, its opportunities, and its societal risks.

Outreach talk on AI and science for more than 700 high-school students, focused on the risks of AI systems beyond large language models.

This work evaluates how LLMs handle mental health crises, introducing a unified taxonomy, benchmark dataset, and expert-based evaluation protocol — revealing both support capabilities and significant safety risks.

Seminar on algorithmic bias, discrimination risks, and fairness-aware AI systems in high-stakes decision-making contexts.

PhD thesis proposing a sociotechnical framework for Trustworthy AI, with contributions in fairness (FairShap), structural disparity in networks (ERG), human–AI complementarity for matching, and AI governance in labor law.

CoMatch is a collaborative matching system that combines human decisions with algorithmic decisions to outperform humans or algorithms alone.

Deep Ensembles can improve performance but may also introduce fairness issues, unevenly benefiting different groups. This study identifies that effect, propose a explanation based on predictive diversity and explores mitigation techniques that can reduce unfairness while preserving performance gains.