Machine Learning (ML) is increasingly used to drive the operation of complex distributed systems deployed on the cloud-edge continuum making their behavior non-deterministic. Their increasing adoption is urgently calling for assurance solutions assessing their non-functional properties (e.g., fairness, robustness, privacy) with the aim of improving trustworthiness. Certification has been clearly identified by policymakers, regulators, and industrial stakeholders as the reliable assurance solution to address this pressing need. Unfortunately, existing certification schemes are not immediately applicable to systems whose non-deterministic behavior is built on ML models. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues, and proposes a first certification scheme for ML-based distributed systems behavior.

翻译：机器学习（ML）正越来越多地用于驱动部署在云-边连续体上的复杂分布式系统的运行，使其行为呈现非确定性。此类系统的日益普及迫切呼唤能够评估其非功能属性（如公平性、鲁棒性、隐私性）的保障方案，以提升可信度。政策制定者、监管机构及产业界已明确将认证视为应对这一紧迫需求的可信保障方案。遗憾的是，现有认证方案无法直接适用于其非确定性行为依赖于ML模型的系统。本文分析了当前认证方案面临的挑战与不足，探讨了开放的研究问题，并首次提出面向ML驱动的分布式系统行为的认证方案。