11 papers
SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention
Lidia Losavio, Francesco Sovrano, Dario Fenoglio +2
Money laundering threatens financial stability and exposes institutions to penalties, motivating automated detection. Because laundering schemes often emerge through relational pat…
Security and Privacy in Agentic AI: Grand Challenges and Future Directions
Adam Jenkins, Agnieszka Kitkowska, Caterina Maidhof +22
We present key challenges and future research directions in the security and privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading intern…
Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation
Francesco Sovrano, Gabriele Dominici, Marc Langheinrich
A central goal of explainable AI is to express large language model (LLM) decision logic symbolically and ground it in internal mechanisms. Existing rule-extraction methods usually…
Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP
Francesco Sovrano
Large language models (LLMs) can amplify misinformation, undermining societal goals such as the UN SDGs. We study three documented drivers of misinformation (valence framing, infor…
Mitigating Prompt-Induced Cognitive Biases in General-Purpose AI for Software Engineering
Francesco Sovrano, Gabriele Dominici, Alberto Bacchelli
Prompt-induced cognitive biases are changes in a general-purpose AI (GPAI) system's decisions caused solely by biased wording in the input (e.g., framing, anchors), not task logic.…
Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements
Francesco Sovrano, Giulia Vilone, Michael Lognoul
Explainable AI (XAI) has evolved in response to expectations and regulations, such as the EU AI Act, which introduces regulatory requirements on AI-powered systems. However, a pers…