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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.07496 |
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| _version_ | 1866913734511820800 |
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| author | Tlaie, Alejandro Farrell, Jimmy |
| author_facet | Tlaie, Alejandro Farrell, Jimmy |
| contents | This paper examines the critical challenges and potential solutions for conducting secure and effective external evaluations of general-purpose AI (GPAI) models. With the exponential growth in size, capability, reach and accompanying risk of these models, ensuring accountability, safety, and public trust requires frameworks that go beyond traditional black-box methods. The discussion begins with an analysis of the need for deeper-than-black-box evaluations (Section I), emphasizing the importance of understanding model internals to uncover latent risks and ensure compliance with ethical and regulatory standards. Building on this foundation, Section II addresses the security considerations of remote evaluations, outlining the threat landscape, technical solutions, and safeguards necessary to protect both evaluators and proprietary model data. Finally, Section III synthesizes these insights into actionable recommendations and future directions, aiming to establish a robust, scalable, and transparent framework for external assessments in GPAI governance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Securing External Deeper-than-black-box GPAI Evaluations Tlaie, Alejandro Farrell, Jimmy Computers and Society This paper examines the critical challenges and potential solutions for conducting secure and effective external evaluations of general-purpose AI (GPAI) models. With the exponential growth in size, capability, reach and accompanying risk of these models, ensuring accountability, safety, and public trust requires frameworks that go beyond traditional black-box methods. The discussion begins with an analysis of the need for deeper-than-black-box evaluations (Section I), emphasizing the importance of understanding model internals to uncover latent risks and ensure compliance with ethical and regulatory standards. Building on this foundation, Section II addresses the security considerations of remote evaluations, outlining the threat landscape, technical solutions, and safeguards necessary to protect both evaluators and proprietary model data. Finally, Section III synthesizes these insights into actionable recommendations and future directions, aiming to establish a robust, scalable, and transparent framework for external assessments in GPAI governance. |
| title | Securing External Deeper-than-black-box GPAI Evaluations |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2503.07496 |