We return to Stanford University for an event hosted in partnership with the Brown Institute for Media Innovation ( http://brown.stanford.edu ) and Stanford’s Computational Journalism Lab ( http://cjlab.stanford.edu/ ). Ted Han, lead developer at DocumentCloud, will discuss the investigative insights that can be found from text-mining documents and public records uploaded and published by journalists. Brown Fellow Rebecca Weiss explains how Brown-funded Project SearchLight uses text mining and data science to discover algorithmic bias in search engine results and political advertisements .



Join us for free pizza and presentations.



Agenda:



6:30-7p: Networking, free pizza



7p -7:15: Welcome from Maneesh Agrawala, Director of the Brown Institute for Media Innovation and Stanford Professor of Computer Science



7:15-7:30: Ted Han, Lead Developer at DocumentCloud



7:30-7:45: Brown Fellow Rebecca Weiss



7:45-8:00: Q&A led by Dan Nguyen, Lecturer, Stanford Journalism Program.



8:00-8:30: More networking!



Speakers:



Ted Han, DocumentCloud Lead Developer



Ted has been leading DocumentCloud's technology efforts with a product focus since 2011. He studied computational linguistics and has worked in technology and startups for more than a decade. The Knight-funded DocumentCloud project is an open-source platform that has allowed journalists to upload, analyze, annotate, and publish more than a million documents, everything from local public records to files from the Guardian’s Edward Snowden leaks.



Rebecca Weiss, Brown Institute Fellow and Stanford PhD Communication candidate



Rebecca is working on SearchLight, a Brown Funded Project that provides an open-source platform for discovering algorithmic bias in search engine results. Specifically, she and her collaborators are seeking ways to identify “partisan profiling,” where search results are more likely to cater to a specific political ideology given the content of search queries and individual attributes of the user.