Jonathan Stray is a Senior Scientist at the Center for Human Compatible AI at UC Berkeley, where he works on the design of recommender systems for better personalized news and information. He teaches the dual masters degree in computer science and journalism at Columbia University, and previously worked as an editor at the Associated Press and a research scientist in the tech industry.

Jonathan Stray

Experiments are the Best Kind of Transparency

What is Media Diversity and Do Recommender Systems Have It?

What’s the Difference Between Search and Recommendation?

Remote Query Execution: A Powerful Way to do Privacy-Protecting Research on Platform Data

Making Amplification Measurable

From “Filter Bubbles”, “Echo Chambers”, and “Rabbit Holes” to “Feedback Loops”

A Menu of Recommender Transparency Options

What Will “Amplification” Mean in Court?

Governing Access to Synthetic Media Detection Technology

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Jonathan Stray