Commentary
India & the Olympics of AI
Allen Lab Fellow Jeremy McKey reflects on India’s AI Impact Summit, exploring the theme of diffusion and the implications for sovereignty and democracy.
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 Fellow Jeremy McKey reflects on India’s AI Impact Summit, exploring the theme of diffusion and the implications for sovereignty and democracy.
Commentary
Allen Lab Researcher David Riveros Garcia draws on his experience building civic technology to fight corruption in Paraguay to make the case that effective civic technology must include power and collective action in its design.
Additional Resource
Ensuring public opinion and policy preferences are reflected in policy outcomes is essential to a functional democracy. A growing ecosystem of deliberative technologies aims to improve the input-to-action loop between people and their governments.
Commentary
Allen Lab Fellow Jeremy McKey reflects on India’s AI Impact Summit, exploring the theme of diffusion and the implications for sovereignty and democracy.
Additional Resource
Ensuring public opinion and policy preferences are reflected in policy outcomes is essential to a functional democracy. A growing ecosystem of deliberative technologies aims to improve the input-to-action loop between people and their governments.
Occasional Paper
In a new working paper, Crocodile Tears: Can the Ethical-Moral Intelligence of AI Models Be Trusted?, Allen Lab authors Sarah Hubbard, David Kidd, and Andrei Stupu introduce an ethical-moral intelligence framework for evaluating AI models across dimensions of moral expertise, sensitivity, coherence, and transparency.