“Personalization, Bias and Privacy ”
Personalization can be seen as a positive bias towards each user. However, it also has negative consequences such as privacy loss as well as the filter bubble effect due to the feedback-loop that creates. In addition, the web system itself can bias the user interaction distorting the data used for personalization, particularly due to exposure bias. Our own biases also affects the personalization process, especially activity bias. Privacy also depends on the personalization level and the personalization level depends on the amount of interaction data available. In this presentation we discuss the interaction of these three elements: personalization, bias and privacy.
Date and Time: 08/01/2020, 18:00 – 19:00 EET
Speaker: Prof. Ricardo Baeza-Yates
Ricardo Baeza-Yates is a Research Fellow at the Roux Institute of Experiential AI of Northeastern University, being from 2017 to 2020 the Director of Data Science at the Silicon Valley campus. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. In 2018 he obtained the Spanish National Awards in Applied Computer Science. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, fairness in AI, data science and algorithms in general.