Salvatore Ruggieri is Associate Professor at the Computer Science Department of the University of Pisa, and he is currently the director the
Master Programme in Business Informatics. He holds a Ph.D. in Computer Science (1999), whose thesis has been awarded by the Italian Chapter
of EATCS as the best Ph.D. thesis in Theoretical Computer Science. He has been the treasurer of the Italian Association for Artificial Intelligence (2003-2007),
and the program chair of the XIII Italian Symposium on Artificial Intelligence, Pisa 10-12 December 2014. He was the coordinator of Enforce, a national FIRB
(Italian Fund for Basic Research) young researcher project on Computer science and legal methods for enforcing the personal rights of non-discrimination
and privacy in ICT systems (ENFORCE, 2010-2014, enforce.di.unipi.it). Salvatore regularly participates in the program committee of top conferences such as
KDD, ECML-PKDD, ICDM, and he has been the guest editor of two special issues: Intelligenza Artificiale journal on Artificial Intelligence for Society and Economy,
June 2015; and Artificial Intelligence and Law journal on Computational Methods for Enforcing Privacy and Fairness in the Knowledge Society, June 2014.
He is a member of the KDD LAB research group, a joint initiative of the University of Pisa and the National Research Council (www-kdd.isti.cnr.it), with research
interests focused in the data mining and knowledge discovery area, including: discrimination measurement, segregation discovery, fairness in classification,
interplay between privacy and fairness, languages and systems for modelling the process of knowledge discovery; sequential and parallel classification
algorithms; frequent itemset mining; web mining and personalization; and applications (CRM, operational risk). Past research topics include program verification
and termination methods, constraint programming, quantified linear systems, intelligent multimedia presentation systems, software quality models.

SWS, RG1   Info

MPI Audience

English

60 Minutes

Saarbrücken

The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena.
Data analysis techniques have been proposed in the last fifty years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination
analysis is a multi-disciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, computational issues. The objective of the talk is
to provide first an introduction on concepts, problems, application areas, datasets, methods and approaches from a multidisciplinary perspective; and then to deep in the data-driven approach
based on data mining for discrimination discovery and prevention.
Reference:
· A. Romei, S. Ruggieri. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review. Vol. 29, Issue 5, November 2014, 582-638.

Claudia Richter

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