AI 中文总结
本文研究传统计算机容错与AI准确性提升机制的关联，扩展可信性分类法并提出AI输出故障概念，映射两类机制的相似性，旨在建立AI系统统一可信性理解。
AI 中文摘要
本文探讨传统计算机系统中的容错机制与基于AI服务的准确性提升方法之间的关联。研究发现，集成（ensembles）、弃权（reject option）等AI机制，与硬件和软件可信性领域的N模冗余（N-modular redundancy）、验收测试具有直接对应关系，尽管二者的动机、依据和实现存在显著差异。我们扩展了传统可信性分类法，纳入基于AI的服务中故障与失效的关键定义特征，提出将AI的错误输出定义为错误，即便系统硬件和软件均无错误运行，AI也成为需单独考虑可信性的第三系统层（位于硬件和软件之后），且其可信性具有特定特征。现有可信性分类法中的故障类别不适用于AI，因此我们提出引入AI输出故障（AI Output Faults），代表AI算法固有的可能不正确（因此属于故障）的结果。随后，我们对容错机制与AI准确性提升机制进行映射和比较，发现二者具有高度相似性。本文研究成果有望助力建立对现代基于AI系统的真正集成且统一的可信性理解。
英文摘要
In this paper we discuss the connection between fault-tolerance mechanisms in traditional computer systems, and approaches in accuracy enhancement for AI-based services. We will find that AI mechanisms such as ensembles and reject option have direct counterparts in hardware and software dependability through N-modular redundancy and acceptance tests, even though their motivation, justification and implementation are quite different. We augment the traditional dependability taxonomy to include critical defining features of faults and failures in AI-based services. We propose to consider incorrect outcomes from AI as errors, even if the system hardware and software operates error free. AI then becomes a third system layer (after hardware and software) for which dependability needs to be considered, and for which dependability has specific characteristics. The existing fault classes in the dependability taxonomy are not suited for AI, and we propose to introduce AI Output Faults, representing the inherent possibly incorrect (and therefore faulty) outcome of AI algorithms. We then map and compare fault tolerance mechanisms with AI accuracy enhancement mechanisms, and we see they carry strike resemblances. We hope the work presented in this paper will help in establishing a truly integrated and unified understanding of dependability for modern-day AI-based systems.
