9 citations · 10 across the 6 of their papers we have counts for
8 papers
Measuring AI harms with multidimensional Lorenz Zonoids
Paolo Giudici, Jose' Maria Sarabia, Sofia Vei
While AI systems increasingly shape high-stakes societal domains, their governance is limited by the lack of risk management methods that operate on real harms, taking their severi…
Personalized Causal Recourse: A Human-In-The-Loop Approach
Denise Tampieri, Giovanni De Toni, Paolo Giudici
Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios…
I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models
Barbara Tarantino, Gennaro Auricchio, Paolo Giudici
Deep learning models are increasingly used in scientific prediction tasks where strong benchmark performance is often interpreted as evidence of scientifically meaningful behavior.…
ISAAC: Auditing Causal Reasoning in Deep Models for Drug-Target Interaction
Barbara Tarantino, Sun Kim, Yijingxiu Lu +1
Deep learning models for drug--target interaction (DTI) prediction often achieve strong benchmark performance without necessarily relying on mechanistically meaningful molecular fe…
AI Harmonics: a human-centric and harms severity-adaptive AI risk assessment framework
Sofia Vei, Paolo Giudici, Pavlos Sermpezis +2
The absolute dominance of Artificial Intelligence (AI) introduces unprecedented societal harms and risks. Existing AI risk assessment models focus on internal compliance, often neg…
Building crypto portfolios with agentic AI
Antonino Castelli, Paolo Giudici, Alessandro Piergallini
The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility. To address the challenge of managing dynamic po…
Group Shapley with Robust Significance Testing and Its Application to Bond Recovery Rate Prediction
Jingyi Wang, Ying Chen, Paolo Giudici
We propose Group Shapley, a metric that extends the classical individual-level Shapley value framework to evaluate the importance of feature groups, addressing the structured natur…
A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
Gavin Abercrombie, Djalel Benbouzid, Paolo Giudici +13
This paper introduces a collaborative, human-centred taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, ty…