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.
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.