Runtime monitoring for Responsible Machine Learning using Model-driven Engineering

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.

As part of this research, we are proposing a novel approach for the runtime monitoring of multiple human-centric requirements. This approach leverages model-driven engineering to more comprehensively monitor ML components.

Publications

Project Lead

Hira Naveed (PhD Candidate)

Project Team

Prof. John Grundy, Dr Chetan Arora, Dr Omar Haggag, Dr Hourieh Khalajzadeh

Human-centric mde diagram