  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.
