Data Systems Seminar Series • The Limitations of Data, Machine Learning & Us

Please note: This seminar will take place in DC 1304.

Ricardo Baeza-Yates, Professor
Khoury College of Computer Sciences, Northeastern University
Department of Engineering, Universitat Pompeu Fabra
Department of Computer Science, Universidad de Chile

Machine learning (ML), particularly deep learning, is being used everywhere. However, not always is it used well, ethically and scientifically.

In this talk we first do a deep dive in the limitations of supervised ML and data, its key component. We cover small data, datification, bias, predictive optimization issues, evaluating success instead of harm, and pseudoscience, among other problems.  The second part is about our own limitations using ML, including different types of human incompetence: cognitive biases, unethical applications, no administrative competence, misinformation, and the impact on mental health. In the final part we discuss regulation on the use of AI and responsible AI principles, that can mitigate the problems outlined above.

Bio: Ricardo Baeza-Yates is a Visiting Professor in the Khoury College of Computer Sciences at the Silicon Valley campus of Northeastern University as well as part-time professor at the departments of Engineering of Universitat Pompeu Fabra and Computer Science of University of Chile. 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. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow. He has won national scientific awards in Chile and Spain, among other accolades and distinctions.

He obtained a Ph.D. in CS from the University of Waterloo, Canada, and his areas of expertise are responsible AI, web search and data mining plus data science and algorithms in general.