AI-Curated Democratic Discourse (Stacky)
Dates: – Present | Location: CLSP @ Johns Hopkins University
Collaborators: Jason Eisner, Andrew Perrin, Daniel Khashabi, Ziang Xiao, Brian Lu, Qingcheng Zeng, Yuqi Li, Kateryna Morhun, Reva Hirave, Tsugunobu Miyake, Will Jurayj, Kevin Xu, Tom Wang, Allen Shen
We seek ways to make the social media experience more prosocial. We will develop a new user interface designed to increase the rate of substantive and constructive conversations, including conversations across political differences and conversations between like-minded strangers.
Specifically, we will use generative AI to:
augment the current conversation by showing relevant high-quality posts from other conversations.
react to a user’s draft post with advice and simulated replies while they are still writing it.
This design goes beyond the traditional threaded conversation model (Usenet, Reddit, Facebook, Twitter, Nextdoor) where disjoint conversations grow one post at a time. It situates posts in a broader curated landscape of viewpoints and supporting information. Users have varying reasons to use social media, but we conjecture that they will sometimes look at good argumentation and competing viewpoints if we make these easy and enjoyable to see. User interfaces shape user behavior. In this more diverse landscape, posters will have to raise their game. They will be challenged more often by direct responses from strangers, or indirectly by the automatic display of related posts alongside theirs. Thus it becomes harder for them to get away with lazy or specious arguments. We will help them as they write their posts, by previewing simulated reactions and offering suggestions before they submit.