With the advancements in machine learning (ML) methods and compute resources,
artificial intelligence (AI) empowered systems are becoming a prevailing
technology. However, current AI technology such as deep learning is not
flawless. The significantly increased model complexity and data scale incur
intensified challenges when lacking trustworthiness and transparency, which
could create new risks and negative impacts. In this paper, we carve out AI
maintenance from the robustness perspective. We start by introducing some
highlighted robustness challenges in the AI lifecycle and motivating AI
maintenance by making analogies to car maintenance. We then propose an AI model
inspection framework to detect and mitigate robustness risks. We also draw
inspiration from vehicle autonomy to define the levels of AI robustness
automation. Our proposal for AI maintenance facilitates robustness assessment,
status tracking, risk scanning, model hardening, and regulation throughout the
AI lifecycle, which is an essential milestone toward building sustainable and
trustworthy AI ecosystems.

本文介绍了从强健性的角度出发，将维护人工智能（AI）的重要性与汽车保养做类比，提出了一种 AI 模型检查框架以检测和减轻其强健性风险，并通过对车辆自主性的借鉴，定义了强健性自动化的级别，以实现 AI 维护的各项功能，从而实现在 AI 生命周期过程中，进行强健性评估、状态追踪、风险扫描、模型加固和监管，这是构建可持续和值得信赖的 AI 生态系统的重要里程碑。