A framework for assessing algorithmic discrimination risks in training data: a case of pediatric type 1 diabetes.

Area of Science:

• Machine Learning
• Medical Informatics
• Algorithmic Fairness

Background:

• Heterogeneous real-world data in medical informatics can introduce biases into machine learning models.
• Subgroup imbalances within training datasets are a primary source of algorithmic discrimination.
• Standard performance evaluations may not detect these data-induced biases.

Purpose of the Study:

• To develop a generalizable framework for identifying algorithmic discrimination risks.
• To assess risks arising from subgroup imbalances in machine learning training data.
• To enhance the reliability and transparency of machine learning systems in medical applications.

Main Methods:

• A 4-step methodology involving controlled representation sampling and ensemble-based model training.
• Systematic perturbation of training subgroup composition while keeping evaluation sets constant.
• Multilevel subgroup disparity quantification and mitigation-oriented interpretation.

Main Results:

• Representation balance alone does not ensure stable or equitable model outputs.
• Ensemble analyses showed subgroup-dependent volatility, impacting model generalization and stability.
• Findings demonstrate structural sensitivity to data composition beyond standard performance metrics.

Conclusions:

• The developed framework exposes hidden disparities and instability patterns in training datasets.
• It quantifies representation-driven behaviors and subgroup-specific training value.
• This diagnostic tool aids in understanding and mitigating data-induced risks before model deployment.