The Last Vote: A Multi-Stakeholder Framework for Language Model Governance

TL;DR

This paper proposes a comprehensive, multi-stakeholder framework for AI governance that addresses systemic democratic risks through novel risk assessment tools and phased institutional strategies.

Contribution

It introduces a new democratic risk taxonomy, a stakeholder-adaptive Incident Severity Score, and a phased implementation approach for AI governance.

Findings

Developed a seven-category democratic risk taxonomy.

Created a stakeholder-adaptive Incident Severity Score.

Outlined a phased strategy for institutional change.

Abstract

As artificial intelligence systems become increasingly powerful and pervasive, democratic societies face unprecedented challenges in governing these technologies while preserving core democratic values and institutions. This paper presents a comprehensive framework to address the full spectrum of risks that AI poses to democratic societies. Our approach integrates multi-stakeholder participation, civil society engagement, and existing international governance frameworks while introducing novel mechanisms for risk assessment and institutional adaptation. We propose: (1) a seven-category democratic risk taxonomy extending beyond individual-level harms to capture systemic threats, (2) a stakeholder-adaptive Incident Severity Score (ISS) that incorporates diverse perspectives and context-dependent risk factors, and (3) a phased implementation strategy that acknowledges the complex institutional changes required for effective AI governance.