Hoy contaremos con la participación de Sara Hajian en el Taller#4 de #geoartivismos. Aquí info sobre ella y sobre su intervención:
Sara Hajian, Ph.D., is a Researcher at Eurecat Technology Center, Barcelona, Spain. She received her Ph.D. degree from Computer Engineering and Maths Department of the Universitat Rovira i Virgili (URV) in June 2013. She received her M.Sc. degree in Computer Science from Iran University of Science and Technology (IUST) in 2008. She also had been a member of APA-IUTcert, an academic research and development center in the area of Network Security Vulnerabilities and Incident Handling (2008-2010). Her research interests are data mining methods and algorithms, social media and social network analysis, privacy-preserving data mining and publishing, and algorithmic bias (discovery and prevention of discrimination).
She has been a visiting student at the Knowledge Discovery and Data Mining Laboratory (KDD-Lab), a joint research group of the Information Science and Technology Institute of the Italian National Research Council (CNR) in Pisa and the Computer Science Department of the University of Pisa (2011). She has been a visiting scientist at Yahoo! Labs in Barcelona (2013-2014). The results of her research have been published in top journals and conferences such as ECML PKDD, IEEE TKDE and DAMI.
In the information society, massive and automated data collection occurs as a consequence of the ubiquitous digital traces we all generate in our daily life. The availability of such wealth of data makes its publication and analysis highly desirable for a variety of purposes, including policy making, planning, marketing, research, etc. Yet, the real and obvious benefits of data analysis and publishing have a dual, darker side. There are at least two potential threats for individuals whose information is published: privacy invasion and potential discrimination. Privacy invasion occurs when the values of published sensitive attributes can be linked to specific individuals (or companies). Discrimination is unfair or unequal treatment of people based on membership to a category, group or minority, without regard to individual characteristics.
On the legal side, parallel to the development of privacy legislation, anti-discrimination legislation has undergone a remarkable expansion, and it now prohibits discrimination against protected groups on the grounds of race, color, religion, nationality, sex, marital status, age and pregnancy, and in a number of settings, like credit and insurance, personnel selection and wages, and access to public services. On the technology side, efforts at guaranteeing privacy have led to developing privacy preserving data mining (PPDM) and efforts at fighting discrimination have led to developing anti-discrimination techniques in data mining. Some proposals are oriented to the discovery and measurement of discrimination, while others deal with preventing data mining (DPDM) from becoming itself a source of discrimination, due to automated decision making based on discriminatory models extracted from inherently biased datasets. I will describe some of these techniques for discrimination prevention, simultaneous discrimination and privacy protection, and discrimination discovery and show some recent results.