Using HCI to Tackle Race and Gender Bias in ADHD Diagnosis

TL;DR

This paper emphasizes the importance of addressing racial and gender biases in ADHD diagnostic technology, proposing considerations for HCI researchers to improve fairness and accuracy.

Contribution

It highlights the impact of racial and gender stereotypes on ADHD diagnosis and offers guidance for HCI research to mitigate these biases.

Findings

Biases affect diagnostic accuracy

Need for inclusive design in diagnostic tools

Recommendations for future HCI studies

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a behavioral disorder that impacts an individual's education, relationships, career, and ability to acquire fair and just police interrogations. Yet, traditional methods used to diagnose ADHD in children and adults are known to have racial and gender bias. In recent years, diagnostic technology has been studied by both HCI and ML researchers. However, these studies fail to take into consideration racial and gender stereotypes that may impact the accuracy of their results. We highlight the importance of taking race and gender into consideration when creating diagnostic technology for ADHD and provide HCI researchers with suggestions for future studies.