Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project

TL;DR

This paper reviews Open AI's Democratic Inputs program, analyzing its assumptions and outcomes to explore scalable public participation methods in AI development and highlight the diversity of public engagement approaches.

Contribution

It critically examines the shared assumptions of the Democratic Inputs program and discusses the implications for inclusive and scalable public participation in AI.

Findings

Participation often assumes scalability and a single model.

There is an emphasis on consensus and abstract principles.

Public participation should include diverse and alternative approaches.

Abstract

In 2023, Open AIs Democratic Inputs program funded 10 teams to design procedures for public participation in generative AI. In this Perspective, we review the results of the project, drawing on interviews with some of the teams and our own experiences conducting participation exercises, we identify several shared yet largely unspoken assumptions of the Democratic Inputs program 1. that participation must be scalable 2. that the object of participation is a single model 3. that there must be a single form of participation 4. that the goal is to extract abstract principles 5. that these principles should have consensus 6. that publics should be representative and encourage alternative forms of participation in AI, perhaps not undertaken by tech companies.