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arXiv AI By Rong Pan, Yili Hong, Min Xie

Reliability Engineering for AI Systems: Challenges, Methods, and Directions

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The paper "Reliability Engineering for AI Systems: Challenges, Methods, and Directions" discusses how AI reliability extends beyond correct outputs to include consistent operation of retrieval, memory, tool use, permissions, human oversight, and inter-system interactions, especially for generative AI. It adapts traditional reliability engineering techniques—such as failure definitions, operational envelopes, FMEA, accelerated testing, field monitoring, and reliability growth—to AI, proposing a four-level diagnostic framework and a TEVV (Test, Evaluation, Verification, Validation) approach. The authors also introduce the SMART framework for statistical guidance and reference NIST’s AI risk management framework, illustrating their concepts with three case studies and emphasizing the urgent need for new measurements and safety guardrails as AI systems evolve rapidly.

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