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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2601.12646 |
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| _version_ | 1866912831924862976 |
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| author | Tran, Ha-Chi |
| author_facet | Tran, Ha-Chi |
| contents | The rapid proliferation of artificial intelligence (AI) has exposed significant deficiencies in risk governance. While ex-ante harm identification and prevention have advanced, Responsible AI scholarship remains underdeveloped in addressing ex-post liability. Core legal questions regarding liability allocation, responsibility attribution, and remedial effectiveness remain insufficiently theorized and institutionalized, particularly for transboundary harms and risks that transcend national jurisdictions. Drawing on contemporary AI risk analyses, we argue that such harms are structurally embedded in global AI supply chains and are likely to escalate in frequency and severity due to cross-border deployment, data infrastructures, and uneven national oversight capacities. Consequently, territorially bounded liability regimes are increasingly inadequate. Using a comparative and interdisciplinary approach, this paper examines compensation and liability frameworks from high-risk transnational domains - including vaccine injury schemes, systemic financial risk governance, commercial nuclear liability, and international environmental regimes - to distill transferable legal design principles such as strict liability, risk pooling, collective risk-sharing, and liability channelling, while highlighting potential structural constraints on their application to AI-related harms. Situated within an international order shaped more by AI arms race dynamics than cooperative governance, the paper outlines the contours of a global AI accountability and compensation architecture, emphasizing the tension between geopolitical rivalry and the collective action required to govern transboundary AI risks effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12646 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unbounded Harms, Bounded Law: Liability in the Age of Borderless AI Tran, Ha-Chi Computers and Society Artificial Intelligence The rapid proliferation of artificial intelligence (AI) has exposed significant deficiencies in risk governance. While ex-ante harm identification and prevention have advanced, Responsible AI scholarship remains underdeveloped in addressing ex-post liability. Core legal questions regarding liability allocation, responsibility attribution, and remedial effectiveness remain insufficiently theorized and institutionalized, particularly for transboundary harms and risks that transcend national jurisdictions. Drawing on contemporary AI risk analyses, we argue that such harms are structurally embedded in global AI supply chains and are likely to escalate in frequency and severity due to cross-border deployment, data infrastructures, and uneven national oversight capacities. Consequently, territorially bounded liability regimes are increasingly inadequate. Using a comparative and interdisciplinary approach, this paper examines compensation and liability frameworks from high-risk transnational domains - including vaccine injury schemes, systemic financial risk governance, commercial nuclear liability, and international environmental regimes - to distill transferable legal design principles such as strict liability, risk pooling, collective risk-sharing, and liability channelling, while highlighting potential structural constraints on their application to AI-related harms. Situated within an international order shaped more by AI arms race dynamics than cooperative governance, the paper outlines the contours of a global AI accountability and compensation architecture, emphasizing the tension between geopolitical rivalry and the collective action required to govern transboundary AI risks effectively. |
| title | Unbounded Harms, Bounded Law: Liability in the Age of Borderless AI |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2601.12646 |