Policy Brief
This report explores the potential of bridging and discusses some of the most common objections, addressing questions around legitimacy and practicality.
Algorithmic ranking and recommendation systems determine what kinds of behaviors are rewarded by digital platforms like Facebook, YouTube, and TikTok by choosing what content to show to users. Because these platforms dominate our attention economy, and because attention can be transformed into money and power, platform recommendations therefore provide a reward structure for society at large.
Platforms currently reward divisive behavior with attention due to the interactions between engagement-based ranking and human psychology. This helps determine the kinds of politicians, journalists, entertainers, and others who can succeed in their respective social arenas, resulting in significant impacts on the quality of our decision-making, our capacity to cooperate, the likelihood of violent conflict, and the robustness of democracy.
We can potentially mitigate this ‘centrifugal’ force toward division by deploying ranking systems that do the opposite—that provide a countervailing ‘centripetal’ or bridging force.
Bridging-based ranking rewards behavior that bridges divides. For example, imagine if Facebook rewarded content that led to positive interactions across diverse audiences, including around divisive topics. How might that change what people, posts, pages, and groups are successful?
This report explores the potential of bridging and discusses some of the most common objections, addressing questions around legitimacy and practicality. It contrasts bridging with some of the most discussed approaches for reforming ranking: reverse-chronological feeds, ‘middleware’, and ‘choose your own ranking system’. (Unfortunately, without introducing bridging, all of these proposed reforms still reward those who seek to divide.) Finally, this report explores early examples where bridging systems are already being tried with some success.
Summary of Next Steps
We can and should rapidly build capacity to develop, evaluate, and deploy bridging-based ranking systems.
Bridging-based ranking alone is not a silver bullet—we need other reforms to address the many challenges of platform-enabled connectivity. But bridging would help address one of the most significant risks—that of being pushed past a “division threshold” beyond which democracy can no longer function.
Commentary
Allen Lab for Democracy Renovation Fellow Dr. Shlomit Wagman lays out a framework to address the threats artificial intelligence poses to global security and democratic institutions.
Additional Resource
In a recent piece for Tech Policy Press, Allen Lab Senior Fellow Alex Pascal and Nathan Sanders outline how US states are well-positioned to lead the development of Public AI. State governments can act as “laboratories of twenty-first century democracy” to experiment with AI applications that directly benefit citizens.
Additional Resource
Over the past several weeks, the Department of Government Efficiency (DOGE) within the Trump Administration has been embedding staff in a range of United States federal agencies. These staff have gained access to data maintained by the federal government. This guide explains what is in the data, what DOGE is doing with it, and why it matters to all Americans.
Feature
What kind of democracy do legislators want? This question was at the center of a recent discussion with Melody Crowder-Meyer, associate professor of political science at Davidson College, as part of the American Politics Speaker Series.
Commentary
At a recent Ash Center panel, experts and AI developers discuss how AI’s influence on politics has evolved over the years. They examine the new tools available to politicians, the role of humans in AI’s relationship with governance, and the values guiding the design of these technologies.
Commentary
Allen Lab for Democracy Renovation Fellow Dr. Shlomit Wagman lays out a framework to address the threats artificial intelligence poses to global security and democratic institutions.
