Many people believe that what social media algorithms choose to show has effects on what people know, how they feel, and how they relate. There is no shortage of ideas about how to do better, but these are hard to test if you don’t work at a platform.

The Prosocial Ranking Challenge is soliciting new content ranking algorithms to test, with $60,000 in prize money split among ten finalists. Finalists will be scored by a panel of expert judges, who will then pick five winners to be tested experimentally.  We can't change platform code, but we’re using a custom browser extension to do the next best thing: fetch as many posts as we can when the page loads and reorder, remove, or add content -- according to your algorithm.

Each winning algorithm will be tested for four months on Facebook, X, and Reddit. We'll collect data on a variety of conflict, well-being, and informational outcomes, including attitudes (via surveys) and behaviors (such as engagement) in a pre-registered, controlled experiment with consenting participants. Testing one ranker costs about $50,000 to recruit and pay enough participants for statistical significance, which we will fund for five winning teams.

The project is run by Jonathan Stray at the Center for Human-compatible AI at UC Berkeley, but it’s an interdisciplinary team effort with researchers, advisors, and judges from many different universities and organizations.

Project Director Bio:

Jonathan Stray is a journalist, a computer scientist, and a conflict scholar. He is currently a Senior Scientist at the Center for Human Compatible AI at UC Berkeley, where he works on the design of recommender algorithms for better-personalized news and information. Previously, he taught the dual master’s degree in computer science and journalism at Columbia University. He has also worked as an editor at the Associated Press, and as an investigative journalist at ProPublica.