Abstract

Machine learning (ML) and artificial intelligence (AI) are not standalone artifacts: they are ecosystems where foundation models are adapted and deployed through layered pipelines spanning developers, platforms, users and regulators. This talk explores how the structure of these ecosystems shapes the distribution of value and risk, and system-level properties like safety and fairness. I begin with a game-theoretic model of the collaboration between general-purpose producers and domain specialists, using it to examine how participation incentives and regulatory design shape equilibrium behaviors. I then connect these formal insights to empirical measurements from 1.86 million open-source AI models, reconstructing lineage networks to quantify how behaviors and failures propagate through fine-tuning processes. I close by outlining my research agenda aimed at building the technical and policy foundations needed to steer collaborative AI ecosystems toward robust, accountable and democratic outcomes.

Video Recording