AI of the People, by the People, for the People: A Social Choice Approach to Collective Control of Artificial Intelligence
For AI governance researchers, this paper offers a conceptual framework to implement and analyze democratic control of AI systems, though it remains at a theoretical level without empirical validation.
The paper proposes a new framework grounded in social choice theory for collective control of AI, arguing that collective input should be incorporated across the ML pipeline and that social choice provides a principled methodology for evaluating control mechanisms.
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.