trustworthy-ai

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.

AI-driven management conflicts with labor laws since AI relies on correlations, not causes, risking legal gaps and hidden discrimination. The study explores these issues and solutions.

This article is a legal and technical study of the intersection between Trustworthy AI and Labour Law. We propose a tripartite taxonomy to understand the implications of the AI Act in the field of Labour Law.