Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-functional properties (e.g., fairness, robustness, privacy) with the aim to improve their trustworthiness. Certification has been clearly identified by policymakers, regulators, and industrial stakeholders as the preferred assurance technique to address this pressing need. Unfortunately, existing certification schemes are not immediately applicable to non-deterministic applications 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 applications.

翻译：机器学习（ML）正越来越多地用于实现具有非确定性行为的高级应用，这些应用运行在云-边缘连续体上。ML的广泛采用迫切需要评估应用非功能性属性（如公平性、鲁棒性、隐私性）的保证解决方案，以提升其可信任度。认证已被政策制定者、监管机构和行业利益相关者明确认定为应对这一迫切需求的首选保证技术。然而，现有的认证方案无法直接应用于基于ML模型的非确定性应用。本文分析了当前认证方案的挑战与不足，探讨了开放的研究问题，并提出了首个针对ML应用的认证方案。