Technical Complexity and Public Discourse: Why It’s So Hard to Write About Data Science
Data science is rarely the subject of in-depth reporting in mainstream publications. Part of the problem: it is extremely difficult to communicate complex technical ideas in plain language to non-experts. In this talk, Meredith Broussard will discuss strategies for communicating with the public with and about data science. Building on the ideas in her new book "Artificial Unintelligence: How Computers Misunderstand the World," she will explain how data journalism can provide guidance to data scientists who seek a wider audience. She will also discuss the limits of computing, arguing against a kind of bias she calls “technochauvinism.” Understanding and articulating the limits of what we *can* do with technology leads to making better choices about what we *should* do with it to make the world better for everyone.
BIO: Meredith Broussard is an assistant professor at the Arthur L. Carter Journalism Institute of New York University, an affiliate at the Moore Sloan Data Science Environment at the NYU Center for Data Science, a 2019 Reynolds Journalism Institute Fellow at the University of Missouri, and the author of "Artificial Unintelligence: How Computers Misunderstand the World." Her research focuses on artificial intelligence in investigative reporting, with a particular interest in using data analysis for social good. Her newest project explores how future historians will read today’s news on tomorrow’s computers. A former features editor at the Philadelphia Inquirer, she has also worked as a software developer at AT&T Bell Labs and the MIT Media Lab. Her features and essays have appeared in The Atlantic, Harper’s, Slate, and other outlets. Follow her on Twitter @merbroussard or contact her via meredithbroussard.com.