Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929290164043776 |
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| author | Shen, Xudong Brown, Hannah Tao, Jiashu Strobel, Martin Tong, Yao Narayan, Akshay Soh, Harold Doshi-Velez, Finale |
| author_facet | Shen, Xudong Brown, Hannah Tao, Jiashu Strobel, Martin Tong, Yao Narayan, Akshay Soh, Harold Doshi-Velez, Finale |
| contents | There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through the lens of two public sector procurement checklists, identifying what we can do now, what should be possible with technical innovation, and what requirements need a more interdisciplinary approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_12609 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities Shen, Xudong Brown, Hannah Tao, Jiashu Strobel, Martin Tong, Yao Narayan, Akshay Soh, Harold Doshi-Velez, Finale Artificial Intelligence Computers and Society There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through the lens of two public sector procurement checklists, identifying what we can do now, what should be possible with technical innovation, and what requirements need a more interdisciplinary approach. |
| title | Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2306.12609 |