Mele was introduced to NLP in 2010 and served for more than four years as our pro bono tech adviser. He helped to envision how technology could move NLP from a hands-on, classroom-based startup to an organization poised to reach national scale online.
In 2014, he connected NLP with Actual Size, the talented Pittsburgh-based creative design studio that became our partner in building the ambitious platform.
“The future is busily arriving,” Mele said. “We've got to be prepared for a radically altered media landscape. Checkology™ is a crucial part of that preparation.”
Mele’s own future arrived on April 25, when he was named director of the Shorenstein Center on Media, Politics and Public Policy at Harvard University’s John F. Kennedy School of Government, where he had previously served as a lecturer and fellow. He returns to Harvard on July 1.
He is currently a senior fellow at the University of Southern California’s Annenberg Center on Communication Leadership & Policy. He is also the co-founder of Internet consulting firm Echo & Co. and the author of the 2013 book “The End of Big: How the Internet Makes David the New Goliath.”
In 2003, at age 26, Mele became webmaster for former Vermont Gov. Howard Dean’s Democratic presidential campaign, where his team used the Internet to fuel a grass-roots social media effort that revolutionized the way money is raised in American politics. The following year, Mele oversaw Internet strategy for Barack Obama’s successful U.S. Senate race in Illinois.
From 2009 to 2014, Mele was a member of the faculty at the Kennedy School, teaching graduate-level classes on the internet and politics. He became senior vice president and deputy publisher of the Los Angeles Times in November 2014; the following year he joined USC, where he also was named the Wallis Annenberg Chair in Journalism at the USC Annenberg School of Journalism.
Mele’s lesson on the checkology™ platform — "Personalizing Information: The Role of Algorithms" — teaches students how algorithms help to find, and to hide, information by determining what people see based on their interests and ideological predispositions. He guides students through a series of interactions with a mock search engine and social media platform to simulate how algorithms can create what is known as a "filter bubble." He concludes the lesson by instructing students on steps they can take to make their personal filter bubbles less restrictive.