arXiv:2605.13362v3 Announce Type: replace-cross Abstract: Computational social choice and algorithmic decision theory offer rich aggregation theory but no end-to-end process for egalitarian self-governance: aggregation, deliberation, amendment, and consensus are each considered in isolation, with key metric-space aggregators being NP-hard. Here, we propose \emph{constitutional governance in metric spaces}, integrating these stages into a protocol for constitutional governance. A community's \emph{legal corpus} comprises its \emph{laws} together with a \emph{constitution}, each being a point in
The increasing complexity of AI-driven decision-making and the growing need for scalable, transparent governance frameworks for autonomous systems necessitates new approaches to computational social choice.
This research outlines a framework for integrating aggregation, deliberation, and consensus into a coherent constitutional governance model, which is crucial for ethical and effective AI agent systems and algorithmic decision theory.
The proposal moves beyond isolated components of computational social choice by integrating them into a comprehensive protocol for constitutional governance, addressing current NP-hard issues in egalitarian self-governance.
- · AI ethicists
- · Developers of AI agent systems
- · Computational social choice researchers
- · Organizations implementing autonomous decision-making
- · Systems with ad-hoc governance structures
- · Simple majority rule systems
- · Designers of isolated aggregation algorithms
The adoption of end-to-end constitutional governance models could lead to more robust and trustworthy autonomous AI systems.
This could accelerate the deployment of AI agents in sensitive domains requiring high levels of transparency and accountability, potentially reshaping white-collar workflows.
Successful implementation of such frameworks might influence future legal and regulatory structures for AI, establishing new norms for digital self-governance.
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Read at arXiv cs.AI