1 citations · 1 across the 8 of their papers we have counts for
Showing cs.AIShow all
2 papers · 1 filter
cs.AI2026
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.…
cs.AI2025
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…