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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” Deadline for online applications: Tuesday, 4 April 2023 at 13:00 CEST Full call for application info: https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbandi.unipi.it%2Fpublic%2FBandi%2FDetail%2F3bd897e5-3872-42f9-87a9-03e1b16156b6&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Cb7f3b0001e244ee4b89b08db18c5c1cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638131009864322326%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=KktP6KWGg5Y3LERJ0a2fJgrupDrxqjuumDOgVSLa2ag%3D&reserved=0 Online applications at https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpica.cineca.it%2Funipi%2Fass-inf2023-1%2F&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Cb7f3b0001e244ee4b89b08db18c5c1cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638131009864322326%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=XTnUqIm4qRyTblHoYMtJ%2BGJ0No3r3NE1ZN1%2FxHeyC4k%3D&reserved=0 (selection code: ass-inf2023-1) Salary: approximately € 2.850 net per month (for non-Italian citizens the non-mandatory health insurance is not included) Duration: 2 years The FINDHR Project: Fairness and Intersectional Non-Discrimination in Human Recommendation (https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Ffindhr.eu%2F&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Cb7f3b0001e244ee4b89b08db18c5c1cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638131009864322326%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=vyBu2lV5bjJYU0XIJMhbersZRglKz9FP45mh66YY2k0%3D&reserved=0) aims to create new ways to ascertain discrimination risk, produce less biased outcomes, and meaningfully incorporate human expertise. Moreover, it aims to create procedures for software development, monitoring and training. The University of Pisa partner of the FINDHR project is the research group KDD Lab (the Knowledge Discovery & Data Mining Lab, https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fkdd.isti.cnr.it%2F&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Cb7f3b0001e244ee4b89b08db18c5c1cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638131009864322326%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=o8%2FhUmssWsKzD6%2FvKV5dcFndh%2FzvY2aaf6a0iO7NoBM%3D&reserved=0) a joint group of ISTI-CNR, Scuola Normale Superiore and Univ. of Pisa, a pioneering research group in data science, fairness, and XAI, established in 1994. Ideal candidates should hold or be about to obtain a PhD degree in Computer Science, Computer Engineering, Mathematics, Physics, Cognitive Sciences or related disciplines, and a proven track record of excellent University grades and publications in relevant top-tier conferences and journals. Background on (some of) the following topics is appreciated: machine learning, deep learning, information retrieval, statistical learning, causal reasoning and learning, counterfactual reasoning, cognitive models of learning and reasoning, human-computer interaction. Excellent written and spoken communication skills in English are required. We are happy if the interested candidates also send us an expression of interest, containing the candidate’s CV accompanied by a letter of motivation and key publications. Please send your expression of interest (not mandatory) to anna.monre...@unipi.it<mailto:anna.monre...@unipi.it> and to salvatore.ruggi...@unipi.it<mailto:salvatore.ruggi...@unipi.it> with subject: [FINDHR] Expression of interest. Recent publications of KDD Lab on Fairness and XAI: * 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. * 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. * O. Lampridis, L. State, R. Guidotti, S. Ruggieri. Explaining short text classification with diverse synthetic exemplars and counter-exemplars. Machine Learning Journal, 2023. * 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. * R. Guidotti, S. Ruggieri. Ensemble of Counterfactual Explainers. Discovery Science (DS 2021). 358-368. Vol. 12986 of LNCS, Springer, October 2021. * 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 * 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. * 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) Please circulate this post in your networks! -- Prof. Salvatore Ruggieri Department of Computer Science University of Pisa, Italy https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fpages.di.unipi.it%2Fruggieri%2F&data=05%7C01%7Cuai%40engr.oregonstate.edu%7Cb7f3b0001e244ee4b89b08db18c5c1cb%7Cce6d05e13c5e4d6287a84c4a2713c113%7C0%7C0%7C638131009864322326%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=JjOEtbqs7kxaktOKVa6VyzfI66WONsA7ZqA%2BTq89RN4%3D&reserved=0
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