  Machine learning (ML) models are becoming integral in healthcare
technologies, presenting a critical need for formal assurance to validate their
safety, fairness, robustness, and trustworthiness. These models are inherently
prone to errors, potentially posing serious risks to patient health and could
even cause irreparable harm. Traditional software assurance techniques rely on
fixed code and do not directly apply to ML models since these algorithms are
adaptable and learn from curated datasets through a training process. However,
adapting established principles, such as boundary testing using synthetic test
data can effectively bridge this gap. To this end, we present a novel technique
called Mix-Up Boundary Analysis (MUBA) that facilitates evaluating image
classifiers in terms of prediction fairness. We evaluated MUBA for two
important medical imaging tasks -- brain tumour classification and breast
cancer classification -- and achieved promising results. This research aims to
showcase the importance of adapting traditional assurance principles for
assessing ML models to enhance the safety and reliability of healthcare
technologies. To facilitate future research, we plan to publicly release our
code for MUBA.
