AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence

TL;DR

This paper introduces a social choice theory-based framework for collective societal control over AI systems, emphasizing multi-stage input integration and principled evaluation criteria.

Contribution

It presents a novel, mathematically grounded approach to collective AI control, extending social choice theory across the entire ML development pipeline.

Findings

Provides a formal social choice model for AI control

Establishes axiomatic criteria for control mechanisms

Offers a comprehensive framework for societal input in AI development

Abstract

With the growing adoption of AI systems, reasoning about how society can exert control over AI becomes an increasingly urgent problem. Existing work on democratic control largely focuses on macro-level governance. In contrast, we propose a new approach grounded in social choice theory, which we term collective control of artificial intelligence. We argue that collective input can and should be incorporated at multiple points across the ML development pipeline, from data collection through objective design to alignment. We further demonstrate that social choice provides a well-suited modelling language for the treatment of collective input across all stages and that its axiomatic methodology yields principled criteria for evaluating various control mechanisms. Overall, our conceptual contribution provides a mathematically grounded framework to implement and analyse collective control of AI systems.