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.
“Can We Trust Fair-AI?” Salvatore Ruggieri, Jose M. Alvarez, Andrea Pugnanna, Laura State, Franco Turini. AAAI Conference on Artificial Intelligence, 2023. [paper]
“Predicting and explaining employee turnover intention.” Matilde Lazzari, Jose M. Alvarez, and Salvatore Ruggieri. International Journal of Data Science and Analytics (IJDSA), 2022. [paper]
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
- IMS International Conference International Conference on Statistics and Data Science; Poster Session; Florence, Italy; December 2022.
- NeurIPS 2022 Workshop on Algorithmic Fairness through the Lens of Causality and Privacy; Poster Session; New Orleans, USA; December 2022.
- Comete Workshop on Ethical AI; Invited Speaker; Palaiseau, France; September 30, 2022.
- ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT); Doctoral Consortium; Seoul, South Korea; June 2022.
- European Workshop on Algorithmic Fairness (EWAF); Lightning Round Presentation; Zurich, Switzerland; June 2022.