Optimizing for What? Algorithmic Amplification and Society

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 Arvind Narayanan. It will take place in-person at Columbia University and online.

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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