A two-year PostDoc position open to join the FINDHR project at the University of Pisa, Italy
Salvatore Ruggieri
A two-year PostDoc position open at the University of Pisa, Italy, to join the FINDHR research project
on the following research objective:
“Explainable and Fair Ranking Methods in Artificial Intelligence for HR Decision Making: advanced eXplainable AI (XAI) paradigms in support of hiring decision making algorithms and processes. XAI methods will be designed and implemented
for post-hoc explanation of fairness-aware ranking algorithms, and for explainable-by-design fairness-aware ranking algorithms. A multi-disciplinary approach will be taken, including legal, ethical, and computational requirements of the designed methods”
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R. Guidotti, A. Monreale, S. Ruggieri, F. Naretto, F. Turini, D. Pedreschi, F. Giannotti. Stable and Actionable Explanations of Black-box Models through Factual and Counterfactual Rules. Data Mining and Knowledge Discovery, 2023.
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S. Ruggieri, J. M. Alvarez, A. Pugnana, L. State, F. Turini. Can We Trust Fair-AI? 37th AAAI Conference on Artificial Intelligence (AAAI 2023). AAAI Press, February 2023.
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O. Lampridis, L. State, R. Guidotti, S. Ruggieri. Explaining short text classification with diverse synthetic exemplars and counter-exemplars. Machine Learning Journal, 2023.
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M. Lazzari, J. M. Alvarez, S. Ruggieri. Predicting and explaining employee turnover intention. International Journal of Data Science and Applications. Vol. 14, Issue 3, September 2022, 279–292.
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R. Guidotti, S. Ruggieri. Ensemble of Counterfactual Explainers. Discovery Science (DS 2021). 358-368. Vol. 12986 of LNCS, Springer, October 2021.
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M. Setzu, R. Guidotti, A. Monreale, F. Turini, D. Pedreschi, F. Giannotti. GLocalX - From Local to Global Explanations of Black Box AI Models. Artificial Intelligence, Volume 294, 2021, 103457
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E. Ntoutsi, et al. Bias in data-driven artificial intelligence systems — An introductory survey. WIREs Data Mining and Knowledge Discovery. Vol. 10, Issue 3, May/June 2020, e1356.
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R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi. 2018. A Survey of Methods for Explaining Black Box Models. ACM Comput. Surv. 51, 5, Article 93 (January 2019)
Salvatore Ruggieri
A two-year PostDoc position open at the University of Pisa, Italy, to join the FINDHR research project
on the following research objective:
“Explainable and Fair Ranking Methods in Artificial Intelligence for HR Decision Making: advanced eXplainable AI (XAI) paradigms in support of hiring decision making algorithms and processes. XAI methods will be designed and implemented
for post-hoc explanation of fairness-aware ranking algorithms, and for explainable-by-design fairness-aware ranking algorithms. A multi-disciplinary approach will be taken, including legal, ethical, and computational requirements of the designed methods”