XAI Reveals the Causes of Attention Deficit Hyperactivity Disorder (ADHD) Bias in Student Performance Prediction

Uncovering algorithmic bias related to sensitive attributes is crucial. However, understanding the underlying causes of bias is even more important to ensure fairer outcomes. This study investigates bias associated with Attention Deficit Hyperactivity Disorder (ADHD) in a machine learning model predicting students' test scores. While fairness metrics did not reveal significant bias, potential subtle bias indicated by variations in model performance for students with ADHD was observed. To uncover causes of this potential bias, we correlated SHapley Additive exPlanations (SHAP) values with the model's prediction errors, identifying the features most strongly associated with increasing prediction errors. Behavioral and self-reported survey features designed to measure students' use of effective learning strategies were identified as potential causes of the model underestimating test grades for students with ADHD. Behavioral features had a stronger correlation between absolute SHAP values and prediction errors (up to r =.354, p =.013) for students with ADHD than for those without ADHD. Students with ADHD often use unique yet effective approaches to studying in online learning environments - approaches that may not be fully captured by traditional measures of typical student behaviors. These insights suggest adjusting feature design to better account for students with ADHD and mitigate bias.