Measurement challenges in AI catastrophic risk governance and safety frameworks
Fuente:
arXiv
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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913525371240448 |
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| author | Kasirzadeh, Atoosa |
| author_facet | Kasirzadeh, Atoosa |
| contents | Safety frameworks represent a significant development in AI governance: they are the first type of publicly shared catastrophic risk management framework developed by major AI companies and focus specifically on AI scaling decisions. I identify six critical measurement challenges in their implementation and propose three policy recommendations to improve their validity and reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00608 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Measurement challenges in AI catastrophic risk governance and safety frameworks Kasirzadeh, Atoosa Computers and Society Safety frameworks represent a significant development in AI governance: they are the first type of publicly shared catastrophic risk management framework developed by major AI companies and focus specifically on AI scaling decisions. I identify six critical measurement challenges in their implementation and propose three policy recommendations to improve their validity and reliability. |
| title | Measurement challenges in AI catastrophic risk governance and safety frameworks |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2410.00608 |