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

Conference Papers

“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

Jose M. Alvarez, Salvatore Ruggieri. “Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference.” Under submission. [arXiv][code]

Jose M. Alvarez, Kristen M. Scott, Salvatore Ruggieri, Bettina Berendt. “Domain Adaptive Decision Trees: Implications for Accuracy and Fairness.” Under submission. [arXiv][code]

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

Work in Progress

“The Initial Ordering Ranking Problem” with Salvatore Ruggieri.

Invited talks, presentations, and workshops