On April 28-29, 2023, the Knight Institute will host a symposium to explore how online amplification works and to consider interventions that would mitigate some of the harms caused by amplification, or allow us to take fuller advantage of the benefits. The symposium, “Optimizing for What? Algorithmic Amplification and Society,” is a collaboration between the Knight Institute and the Institute’s Visiting Senior Research Scientist Arvind Narayanan. It will take place in-person at Columbia University and online.

Please RSVP for event updates.  

Featured papers

A Field Experiment on the Impact of Algorithmic Curation on Content Consumption Behavior

• Fabian Baumann, Postdoctoral Fellow, Max Planck Institute for Human Development
• Philipp Lorenz-Spreen, Research Scientist, Max Planck Institute for Human Development

What's in an Algorithm? Empowering users through nutrition labels for recommender systems

• Luca Belli, Independent Researcher
• Marlena Wisniak, Senior Legal Consultant, European Center for Not-for-Profit Law

Echo Chambers, Rabbit Holes, and Algorithmic Bias: How YouTube recommends content to real users

• Megan Brown, Senior Research Engineer, NYU Center for Social Media and Politics
• James Bisbee, Assistant Professor, Vanderbilt University
• Angela Lai, Ph.D. Candidate, New York University
• Richard Bonneau, Faculty Research Associate, NYU Center for Social Media and Politics
• Jonathan Nagler, Professor of Politics and Co-Director, NYU Center for Social Media and Politics
• Joshua A. Tucker, Professor of Politics and Co-Director, NYU Center for Social Media and Politics

Algorithmic Amplification for Collective Intelligence

• Jason Burton, Assistant Professor, Copenhagen Business School, Alexander von Humboldt Research Fellow, Max Planck Institute for Human Development

A Public Service Media Perspective on the Algorithmic Amplification of Cultural Content

• Fernando Diaz, Research Scientist, Google
• Georgina Born, Professor, University College London

Teachable Agents for End-User Empowerment in Personalized Feed Curation

• Kevin Feng, Ph.D. Student, University of Washington
• David McDonald, Professor, University of Washington
• Amy X. Zhang, Assistant Professor, University of Washington

How Friction-in-Design Moderates, Amplifies, and Dictates Speech and Conduct

• Brett Frischmann, Professor, Villanova Law
• Paul Ohm, Professor, Georgetown University Law Center

The Algorithmic Management of Polarization and Violence on Social Media

• Ravi Iyer, Managing Director, Psychology of Technology Institute
• Jonathan Stray, Senior Scientist, Center for Human-Compatible Artificial Intelligence
• Helena Puig Larrauri, Strategy Lead & Co-Founder, Build Up

It’s the Algorithm: A large-scale comparative field study of news quality interventions

• Benjamin Kaiser, Ph.D. Candidate, Center for Information Technology Policy, Princeton University
• Jonathan Mayer, Assistant Professor, Princeton University

What Makes Algorithmic Amplification Wrongful?

• Benjamin Laufer, Ph.D. Candidate, Cornell Tech
• Helen Nissenbaum, Professor, Cornell Tech

Communicative Justice and the Distribution of Attention

• Seth Lazar, Professor, Australian National University

The Myth of “The Algorithm”: A system-level view of algorithmic amplification

• Kristian Lum, Associate Research Professor, University of Chicago
• Tomo Lazovich, Senior Research Scientist, Institute for Experiential AI at Northeastern University

Emotional and Political Effects of Twitter’s Ranking Algorithm

• Smitha Milli, Postdoctoral Associate, Cornell Tech
• Micah Carroll, Ph.D. Candidate, University of California, Berkeley
• Sashrika Pandey, Undergraduate Researcher, University of California, Berkeley
• Yike Wang, Undergraduate Researcher, University of California, Berkeley
• Anca Dragan, Associate Professor, University of California, Berkeley

Bridging Systems: Open problems for countering destructive divisiveness in ranking, recommenders, and governance

• Aviv Ovadya, Affiliate, Berkman Klein Center, Harvard University
• Luke Thorburn, Ph.D. Candidate, King’s College London

Recommenders with Values: Developing recommendation engines in a public service organization

• Alessandro Piscopo, Lead Data Scientist, BBC Product Group
• Lianne Kerlin, Research Lead, BBC R&D
• North Kuras, Senior UX Architect, BBC Product Group
• James Fletcher, Responsible Data & AI Lead, BBC Product Group
• Calum Wiggins, Executive Product Manager, BBC Product Group
• Anna McGovern, Lead Automated Curation Specialist, BBC Product Group
• Megan Stamper, Head of Data Science, BBC Product Group

Cycles of Symbol Production on Online Platforms

• Inioluwa Deborah Raji, Ph.D. Candidate, University of California, Berkeley
• Fernando Diaz, Research Scientist, Google
• Irene Lo, Assistant Professor, Stanford University