Practical Principles for AI Cost and Compute Accounting
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908456479358976 |
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| author | Casper, Stephen Bailey, Luke Schreier, Tim |
| author_facet | Casper, Stephen Bailey, Luke Schreier, Tim |
| contents | Policymakers increasingly use development cost and compute as proxies for AI capabilities and risks. Recent laws have introduced regulatory requirements for models or developers that are contingent on specific thresholds. However, technical ambiguities in how to perform this accounting create loopholes that can undermine regulatory effectiveness. We propose seven principles for designing AI cost and compute accounting standards that (1) reduce opportunities for strategic gaming, (2) avoid disincentivizing responsible risk mitigation, and (3) enable consistent implementation across companies and jurisdictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15873 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Practical Principles for AI Cost and Compute Accounting Casper, Stephen Bailey, Luke Schreier, Tim Artificial Intelligence Computers and Society Policymakers increasingly use development cost and compute as proxies for AI capabilities and risks. Recent laws have introduced regulatory requirements for models or developers that are contingent on specific thresholds. However, technical ambiguities in how to perform this accounting create loopholes that can undermine regulatory effectiveness. We propose seven principles for designing AI cost and compute accounting standards that (1) reduce opportunities for strategic gaming, (2) avoid disincentivizing responsible risk mitigation, and (3) enable consistent implementation across companies and jurisdictions. |
| title | Practical Principles for AI Cost and Compute Accounting |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2502.15873 |