REVIEW 5 cited by
Generative Social Choice
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The mathematical study of voting, social choice theory, has traditionally only been applicable to choices among a few predetermined alternatives, but not to open-ended decisions such as collectively selecting a textual statement. We introduce generative social choice, a design methodology for open-ended democratic processes that combines the rigor of social choice theory with the capability of large language models to generate text and extrapolate preferences. Our framework divides the design of AI-augmented democratic processes into two components: first, proving that the process satisfies representation guarantees when given access to oracle queries; second, empirically validating that these queries can be approximately implemented using a large language model. We apply this framework to the problem of summarizing free-form opinions into a proportionally representative slate of opinion statements; specifically, we develop a democratic process with representation guarantees and use this process to portray the opinions of participants in a survey about abortion policy. In a trial with 100 representative US residents, we find that 84 out of 100 participants feel "excellently" or "exceptionally" represented by the slate of five statements we extracted.
Forward citations
Cited by 5 Pith papers
-
CALMA: A Process for Deriving Context-aligned Axes for Language Model Alignment
CALMA is a grounded-theory, participatory method for deriving community-specific language model alignment axes from open-ended user interactions and group discussion, piloted with two small groups.
-
Emergence of human-like polarization among large language model agents
Interacting LLM agents form homophilic networks and polarized opinions that mirror human echo-chamber and backfire effects, with individual-level interventions reducing polarization most.
-
Data Sharing with a Generative AI Competitor
In a two-stage data-sharing game, the unique equilibrium is either that the firm shares just enough data to stop the platform buying expert data, or that the firm shares an amount that maximizes its payoff while the p...
-
Designing for Constructive Civic Communication: A Framework for Human-AI Collaboration in Community Engagement Processes
The paper adapts Shneiderman's human-AI framework to civic communication by splitting human control into public leader and community organization control, and offers design considerations for constructive engagement.
-
AI and the Future of Digital Public Squares
A multi-stakeholder agenda argues that LLM-enabled collective dialogue, bridging, moderation, and proof-of-humanity tools can strengthen digital public squares if paired with research and safeguards.
Discussion (0). Continue with ORCID to comment.