Explanation in Multi-Stakeholder Recommendation for Enterprise Decision Support Systems. - Giandomenico Cornacchia; Francesco M. Donini; Fedelucio Narducci; Claudio Pomo; Azzurra Ragone (CAiSE Workshops, 2021)

Improving the User Experience and the Trustworthiness of Financial Services. - Giandomenico Cornacchia; Fedelucio Narducci; Azzurra Ragone (INTERACT (5), 2021)

A General Model for Fair and Explainable Recommendation in the Loan Domain (Short paper). - Giandomenico Cornacchia; Fedelucio Narducci; Azzurra Ragone (KaRS/ComplexRec@RecSys, 2021)

Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness Under Unawareness Setting. - Giandomenico Cornacchia; Vito Walter Anelli; Fedelucio Narducci; Azzurra Ragone; Eugenio Di Sciascio (ECAI, 2023)

Counterfactual Reasoning for Responsible AI Assessment. - Giandomenico Cornacchia; Vito Walter Anelli; Fedelucio Narducci; Azzurra Ragone; Eugenio Di Sciascio (Ital-IA, 2023)

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. - Daniele Malitesta; Giandomenico Cornacchia; Claudio Pomo; Tommaso Di Noia (EvalRS@KDD, 2023)

On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis. - Daniele Malitesta; Giandomenico Cornacchia; Claudio Pomo; Tommaso Di Noia (MMIR@MM, 2023)

Counterfactual Reasoning for Decision Model Fairness Assessment. - Giandomenico Cornacchia; Vito Walter Anelli; Fedelucio Narducci; Azzurra Ragone; Eugenio Di Sciascio (WWW (Companion Volume), 2023)

Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning. - Giandomenico Cornacchia; Vito Walter Anelli; Fedelucio Narducci; Azzurra Ragone; Eugenio Di Sciascio (CoRR, 2023)

Auditing fairness under unawareness through counterfactual reasoning. - Giandomenico Cornacchia; Vito Walter Anelli; Giovanni Maria Biancofiore; Fedelucio Narducci; Claudio Pomo; Azzurra Ragone; Eugenio Di Sciascio (Inf. Process. Manag., 2023)

MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks. - Giandomenico Cornacchia; Giulio Zizzo; Kieran Fraser; Muhammad Zaid Hameed; Ambrish Rawat; Mark Purcell (AIES (1), 2024)

Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI. - Ambrish Rawat; Stefan Schoepf; Giulio Zizzo; Giandomenico Cornacchia; Muhammad Zaid Hameed; Kieran Fraser; Erik Miehling; Beat Buesser; Elizabeth M. Daly; Mark Purcell; Prasanna Sattigeri; Pin-Yu Chen; et al. (CoRR, 2024)

Granite Guardian. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martin Santillan Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (CoRR, 2024)

Granite Guardian: Comprehensive LLM Safeguarding. - Inkit Padhi; Manish Nagireddy; Giandomenico Cornacchia; Subhajit Chaudhury; Tejaswini Pedapati; Pierre L. Dognin; Keerthiram Murugesan; Erik Miehling; Martín Santillán Cooper; Kieran Fraser; Giulio Zizzo; Muhammad Zaid Hameed; et al. (NAACL (Industry Track), 2025)

Underrepresentation, label bias, and proxies: Towards Data Bias Profiles for the EU AI act and beyond. - Marina Ceccon; Giandomenico Cornacchia; Davide Dalle Pezze; Alessandro Fabris; Gian Antonio Susto (Expert Syst. Appl., 2025)

Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs. - Giulio Zizzo; Giandomenico Cornacchia; Kieran Fraser; Muhammad Zaid Hameed; Ambrish Rawat; Beat Buesser; Mark Purcell; Pin-Yu Chen; Prasanna Sattigeri; Kush R. Varshney (CoRR, 2025)

MAD-MAX: Modular And Diverse Malicious Attack MiXtures for Automated LLM Red Teaming. - Stefan Schoepf; Muhammad Zaid Hameed; Ambrish Rawat; Kieran Fraser; Giulio Zizzo; Giandomenico Cornacchia; Mark Purcell (CoRR, 2025)

Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation. - Cécile Rousseau; Tobia Boschi; Giandomenico Cornacchia; Dhaval Salwala; Alessandra Pascale; Juan Bernabé-Moreno (CoRR, 2025)

Formalizing Multimedia Recommendation through Multimodal Deep Learning. - Daniele Malitesta; Giandomenico Cornacchia; Claudio Pomo; Felice Antonio Merra; Tommaso Di Noia; Eugenio Di Sciascio (Trans. Recomm. Syst., 2025)