The Last Vote: A Multi-Stakeholder Framework for Language Model Governance
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917085670539264 |
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| author | Sahoo, Subramanyam Chhawacharia, Aditi |
| author_facet | Sahoo, Subramanyam Chhawacharia, Aditi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Last Vote: A Multi-Stakeholder Framework for Language Model Governance Sahoo, Subramanyam Chhawacharia, Aditi Computers and Society 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. |
| title | The Last Vote: A Multi-Stakeholder Framework for Language Model Governance |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2511.13432 |