My research lies within the fields of machine learning, causality, and ethical AI. I’m mainly interested in causal notions of algorithmic fairness, structural counterfactuals, and knowledge representation.

Conference Papers

“Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference.” Jose M. Alvarez, Salvatore Ruggieri. Forthcoming in ACM EAAMO 2023. [arXiv]

“Fairness implications of encoding protected categorical attributes.” Carlos Mougan, Jose M. Alvarez, Salvatore Ruggieri, and Steffen Staab. ACM AIES 2023. [paper]

“Domain Adaptive Decision Trees: Implications for Accuracy and Fairness.” Jose M. Alvarez, Kristen M. Scott, Bettina Berendt, Salvatore Ruggieri. ACM FAccT 2023. [paper]

“Can We Trust Fair-AI?” Salvatore Ruggieri, Jose M. Alvarez, Andrea Pugnanna, Laura State, Franco Turini. AAAI Conference on Artificial Intelligence 2023. [paper]

Journal Papers

“Predicting and explaining employee turnover intention.” Matilde Lazzari, Jose M. Alvarez, and Salvatore Ruggieri. International Journal of Data Science and Analytics (IJDSA), 2022. [paper]

Working Papers

What’s the Problem, Linda? The Conjunction Fallacy as a Fairness Problem. [arXiv]

The Initial Screening Order Problem with Salvatore Ruggieri. [arXiv]

Other Publications

Proceedings of the 2nd European Workshop on Algorithmic Fairness, Winterthur, Switzerland, June 7th to 9th, 2023. Edited by: Jose M. Alvarez, Alessandro Fabris, Christoph Heitz, Corinna Hertweck, Michele Loi, Meike Zehlike. CEUR Workshop Proceedings 3442, 2023. [EWAF 2023]

Invited talks, presentations, and workshops