8 papers
Bathtubs, Boundaries, and Sandboxes: AI Regulatory Learning under Legal Uncertainty
Tom Deckenbrunnen, Alessio Buscemi, Marco Almada +2
Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rapid development and emergent pro…
LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability
Tom Lucas, Alessio Buscemi, Alfredo Capozucca +2
Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite for responsible AI deployment.…
When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
LLMs-based agents increasingly operate in multi-agent environments where strategic interaction and coordination are required. While existing work has largely focused on individual…
FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
Letting AI agents interact in multi-agent applications adds a layer of complexity to the interpretability and prediction of AI outcomes, with profound implications for their trustw…
Strategic Communication and Language Bias in Multi-Agent LLM Coordination
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
Large Language Model (LLM)-based agents are increasingly deployed in multi-agent scenarios where coordination is crucial but not always assured. Research shows that the way strateg…
The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes
Alessio Buscemi, Thibault Simonetto, Daniele Pagani +3
The systematic assessment of AI systems is increasingly vital as these technologies enter high-stakes domains. To address this, the EU's Artificial Intelligence Act introduces AI R…