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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.22829 |
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| _version_ | 1866909626493042688 |
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| author | Liu, Chenruo Tang, Kenan Qin, Yao Lei, Qi |
| author_facet | Liu, Chenruo Tang, Kenan Qin, Yao Lei, Qi |
| contents | This paper bridges distribution shift and AI safety through a comprehensive analysis of their conceptual and methodological synergies. While prior discussions often focus on narrow cases or informal analogies, we establish two types connections between specific causes of distribution shift and fine-grained AI safety issues: (1) methods addressing a specific shift type can help achieve corresponding safety goals, or (2) certain shifts and safety issues can be formally reduced to each other, enabling mutual adaptation of their methods. Our findings provide a unified perspective that encourages fundamental integration between distribution shift and AI safety research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22829 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies Liu, Chenruo Tang, Kenan Qin, Yao Lei, Qi Machine Learning Artificial Intelligence This paper bridges distribution shift and AI safety through a comprehensive analysis of their conceptual and methodological synergies. While prior discussions often focus on narrow cases or informal analogies, we establish two types connections between specific causes of distribution shift and fine-grained AI safety issues: (1) methods addressing a specific shift type can help achieve corresponding safety goals, or (2) certain shifts and safety issues can be formally reduced to each other, enabling mutual adaptation of their methods. Our findings provide a unified perspective that encourages fundamental integration between distribution shift and AI safety research. |
| title | Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.22829 |