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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14668 |
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| _version_ | 1866909000321204224 |
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| author | deVadoss, John Artzt, Matthias |
| author_facet | deVadoss, John Artzt, Matthias |
| contents | Ensuring that an AI system behaves reliably and as intended, especially in the presence of unexpected faults or adversarial conditions, is a complex challenge. Inspired by the field of Byzantine Fault Tolerance (BFT) from distributed computing, we explore a fault tolerance architecture for AI safety. By drawing an analogy between unreliable, corrupt, misbehaving or malicious AI artifacts and Byzantine nodes in a distributed system, we propose an architecture that leverages consensus mechanisms to enhance AI safety and reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14668 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Byzantine Fault Tolerance Approach towards AI Safety deVadoss, John Artzt, Matthias Distributed, Parallel, and Cluster Computing Ensuring that an AI system behaves reliably and as intended, especially in the presence of unexpected faults or adversarial conditions, is a complex challenge. Inspired by the field of Byzantine Fault Tolerance (BFT) from distributed computing, we explore a fault tolerance architecture for AI safety. By drawing an analogy between unreliable, corrupt, misbehaving or malicious AI artifacts and Byzantine nodes in a distributed system, we propose an architecture that leverages consensus mechanisms to enhance AI safety and reliability. |
| title | A Byzantine Fault Tolerance Approach towards AI Safety |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.14668 |