Algorithmic Fairness in Biomarker‐Based Machine Learning Models to Predict Alzheimer’s Dementia in Individuals with Mild Cognitive Impairment

With increasing availability of large‐scale data sets in biomedical research, machine learning (ML) algorithms rapidly gain importance for research and clinical practice. However, ML models bear the risk of increasing health disparities, whenever trained with imbalanced or biased data. Based on these ethical concerns the WHO guidelines on Artificial Intelligence for Healthcare recommend incorporating algorithmic fairness in ML models by design. Although research on biomarker detection and dementia risk prediction in Alzheimer’s disease increasingly employs ML algorithms, the fairness of these models has not been in focus to date.