  Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial
for clinical decision-making and patient care. In this study, we investigate
the potential presence of age and sex bias in AML subtype classification using
Multiple Instance Learning (MIL) architectures. To that end, we train multiple
MIL models using different levels of sex imbalance in the training set and
excluding certain age groups. To assess the sex bias, we evaluate the
performance of the models on male and female test sets. For age bias, models
are tested against underrepresented age groups in the training data. We find a
significant effect of sex and age bias on the performance of the model for AML
subtype classification. Specifically, we observe that females are more likely
to be affected by sex imbalance dataset and certain age groups, such as
patients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype,
are significantly affected by an age bias present in the training data.
Ensuring inclusivity in the training data is thus essential for generating
reliable and equitable outcomes in AML genetic subtype classification,
ultimately benefiting diverse patient populations.
