As machine learning (ML) components become increasingly integrated into software systems, the emphasis on the ethical or responsible aspects of their use has grown significantly. This includes building ML-based systems that adhere to human-centric requirements, such as fairness, privacy, explainability, well-being, transparency and human values. Meeting these human-centric requirements is not only essential for maintaining public trust but also a key factor determining the success of ML-based systems. However, as these requirements are dynamic in nature and continually evolve, pre-deployment monitoring of these models often proves insufficient to establish and sustain trust in ML components. Runtime monitoring approaches for ML are potentially valuable solutions to this problem. Existing state-of-the-art techniques often fall short as they seldom consider more than one human-centric requirement, typically focusing on fairness, safety, and trust. The technical expertise and effort required to set up a monitoring system are also challenging. In my PhD research, I propose a novel approach for the runtime monitoring of multiple human-centric requirements. This approach leverages model-driven engineering to more comprehensively monitor ML components. This doctoral symposium paper outlines the motivation for my PhD work, a potential solution, progress so far and future plans.

翻译：随着机器学习组件日益融入软件系统，其使用中的道德或负责任方面的重要性显著提升。这包括构建基于机器学习且遵循公平性、隐私性、可解释性、福祉性、透明性及人类价值观等以人为本需求的系统。满足这些以人为本的需求不仅对维护公众信任至关重要，也是决定基于机器学习的系统成功与否的关键因素。然而，由于这些需求具有动态性且持续演化，对这些模型进行部署前监控往往不足以建立并维持对机器学习组件的信任。面向机器学习的运行时监控方法是解决该问题的潜在有效方案。现有最先进技术通常存在不足，因为它们很少同时考虑多个以人为本的需求，而主要聚焦于公平性、安全性和信任性。搭建监控系统所需的技术专长和工作量也颇具挑战性。在我的博士研究中，我提出了一种新颖的针对多个以人为本需求的运行时监控方法。该方法利用模型驱动工程，以更全面地对机器学习组件进行监控。本博士研讨会论文概述了我博士工作的动机、潜在解决方案、当前进展及未来规划。