Title: Bias on Search and Recommender Systems

Abstract

In this presentation we cover all biases that affect search and recommender systems. They include biases on the data, the algorithms as well as the user interaction, in particular the ones related to relevance feedback loops (e.g., ranking and personalization). In each case we cover the main concepts and when known, the techniques to ameliorate them, as well as biases that might be product of the evaluation methods used. This presentation is partially based on Bias on the Web, Communications of ACM, June 2018.

Biography

Ricardo Baeza-Yates is a Research Professor at the Institute for Experiential AI of Northeastern University. He is also a part-time professor at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. 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 to the ACM Council. Since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. 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, bias and ethics on AI, data science and algorithms in general.