Divisiveness appears to be increasing in much of the world. This can lead to concern about political violence and a decreasing capacity to collaboratively address large-scale societal challenges. In this working paper we aim to articulate an interdisciplinary research and practice area focused around what we call bridging systems. These are systems which increase mutual understanding and trust across divides, creating space for productive conflict, deliberation, or cooperation.
We give examples of bridging systems across three domains: recommender systems on social media, software for conducting civic forums, and human-facilitated group deliberation. We argue that these examples can be more meaningfully understood as processes for attention-allocation (as opposed to “content distribution” or “amplification”). Further, we develop a corresponding framework to explore similarities – and opportunities for bridging – across these seemingly disparate domains. In particular, we focus on the potential of bridging-based ranking to bring the benefits of offline bridging into spaces which are already governed by algorithms. Throughout, we suggest research directions that could improve our capacity to incorporate bridging into a world increasingly mediated by algorithms and artificial intelligence.
The year 2024 was dubbed “the largest election year in global history” with half the world’s population voting in national elections. Earlier this year, we hosted an event on AI and the 2024 Elections where scholars spoke about the potential influence of artificial intelligence on the election cycle– from misinformation to threats on election infrastructure. This webinar offered a reflection and exploration of the impacts of technology on the 2024 election landscape.
Earlier this year, the Allen Lab for Democracy Renovation hosted a convening on the Political Economy of AI. This collection of essays from leading scholars and experts raise critical questions surrounding power, governance, and democracy as they consider how technology can better serve the public interest.
As a part of the Allen Lab’s Political Economy of AI Essay Collection, David Gray Widder and Mar Hicks draw on the history of tech hype cycles to warn against the harmful effects of the current generative AI bubble.
