Building Trust: Foundations of Security, Safety and Transparency in AI
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910705232379904 |
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| author | Sidhpurwala, Huzaifa Mollett, Garth Fox, Emily Bestavros, Mark Chen, Huamin |
| author_facet | Sidhpurwala, Huzaifa Mollett, Garth Fox, Emily Bestavros, Mark Chen, Huamin |
| contents | This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the apparent absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper aims to provide some of the foundational pieces for more standardized security, safety, and transparency in the development and operation of AI models and the larger open ecosystems and communities forming around them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Building Trust: Foundations of Security, Safety and Transparency in AI Sidhpurwala, Huzaifa Mollett, Garth Fox, Emily Bestavros, Mark Chen, Huamin Computers and Society Artificial Intelligence Computation and Language This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the apparent absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper aims to provide some of the foundational pieces for more standardized security, safety, and transparency in the development and operation of AI models and the larger open ecosystems and communities forming around them. |
| title | Building Trust: Foundations of Security, Safety and Transparency in AI |
| topic | Computers and Society Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2411.12275 |