Research

My research interests revolve around augmenting human decision-making with AI. This involves evaluating the interaction of the desired outcome with its context. Current practices in data-driven decisioning adopt models that span from regression to neural networks and ensemble trees which, although powerful and mostly informative, only capture correlations in the data. Correlation is hardly enough when trying to understand the effect of actions. What decision-makers need is causality.

My PhD thesis, supervised by Prof. Francesca Toni and titled “Causal Discovery for Trustworthy Artificial Intelligence” worked towards creating transparent techniques for causal discovery and its use in machine learning predictions, leveraging argumentation. Causal discovery is about drawing a graph of the relationships influencing an observed phenomenon. Argumentation is a form of non-monotonic reasoning useful for resolving coflicts in a debate. We created systems that allow humans and machines to debate about causality. This highlights cause-effect relationships, giving decision-makers the levers to drive the outcomes they seek.

Publications