The Art of Skepticism in a Data Driven World--Zoom Event

 The Art of Skepticism in a Data Driven World

Celebrating Social Data Science with Dr. Jevin West
Hosted by Katie Shilton and Brian Kim, co-directors of UMD's new major in Social Data Science

Wednesday, March 30th at 3:00pm EST
Online via Zoom
CLICK HERE TO REGISTER

To celebrate the launch of UMD's new major in Social Data Science, join us for a talk by Dr. Jevin West, associate professor at the Information School at the University of Washington and co-author of Calling Bullshit: The Art of Skepticism in a Data-Driven World.

Social Data Science is the first major at UMD to teach data science techniques alongside social science methods and theories to solve pressing social problems. Dr. West's research is at the forefront of that intersection. He is the co-founder of the new Center for an Informed Public at the University of Washington, aimed at resisting strategic misinformation, promoting an informed society and strengthening democratic discourse. And he has dedicated his teaching to helping students use the tools of data science to  think critically about what constitutes evidence. We are delighted to have him speak as we launch the new major!
 

---

Abstract:

"Today’s world is more thoroughly quantified than ever before. We are awash in numbers, statistics, figures, algorithms, and data graphics. Quantitative information is power: it is used to convey objectivity, to strengthen arguments, and to inform decisions—and all too often it is used to impress, overwhelm, and persuade without any conviction to truth. Fortunately, one doesn’t need an advanced degree in data science (although quite helpful) to see through the vast majority of quantitative misinformation. Drawing on examples from current events of misleading claims and misuses of artificial intelligence, this talk aims to empower data scientists and the everyday information consumer to push back when confronted by numerical arguments and data-hungry technologies. By focusing attention on the data sources and their weaknesses, rather than on the complex and often obscure algorithms and statistical procedures, we can see through the hype and hold our own against those selling quantitative snake-oil."