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人工智能領域研究前沿系列

TITLE:

Science Wants Numbers About People: The Numbers Are Likely Wrong

ABSTRACT:

Science (and industry, government, non-profits, and university sectors) depend on numbers for situational awareness, making decisions based on these numbers. However, what if the numbers are wrong? Unfortunately, when concerning people, the numbers are often wrong. As a case study, we ‘peel back the onion’ on the numbers from a large-scale web traffic study of 86 worldwide websites comparing two web analytics tools. One tool is a site-centric service, and one uses a triangulation of user, site, and network-centric methods. The resulting numbers for the three standard analytics metrics evaluated are statistically different. However, which set of numbers is ‘right’? A deductive analysis shows that both approaches are inaccurate and imprecise, even though advertising and other decisions worth hundreds of billions of US dollars annually are made using these numbers. The talk ends with recommendations to move forward to report numbers about people in this and related contexts.

ID: 84797479305

密碼：270030

參會鏈接: https://us02web.zoom.us/j/84797479305

SPEAKER:

Dr. Jim Jansen is a Principal Scientist in the Social Computing Group of the Qatar Computing Research Institute leading a research team working on automatic segmentation of user data (see https://quecst.qcri.org). He is a graduate of West Point and has a Ph.D. in computer science from Texas A&M University. Professor Jansen is editor-in-chief of the journal, Information Processing & Management and former editor-in-chief of Internet Research. He has received several awards and honors, including an ACM Research Award, six application development awards, a product innovation award, and a university-level teaching award, along with other writing, publishing, research, teaching, and leadership honors. Dr. Jansen has authored or co-authored 350 or so research publications, with articles appearing in a multi-disciplinary range of journals and conferences. He is the author of the book, Data-Driven Personas (Morgan Claypool).

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