The Algorithmic Gaze: AI, Autism, and the Global Power Shift in Diagnosis

Abstract

Artificial intelligence (AI) has rapidly emerged as a promising tool for improving autism spectrum disorder (ASD) screening, particularly in regions where access to trained specialists remains limited. Recent advances in machine learning have demonstrated encouraging diagnostic performance through the analysis of facial expressions, speech, eye gaze, and other behavioral markers, offering the potential to expand early diagnosis and reduce disparities in healthcare access. However, existing research has focused predominantly on algorithm development and diagnostic accuracy, while comparatively little attention has been paid to the broader ethical, cultural, and geopolitical implications of AI-assisted autism diagnosis. Drawing upon perspectives from philosophy of medicine and science and technology studies, this paper examines how AI redistributes power through three interconnected shifts: the transfer of diagnostic authority from clinicians to algorithms, the globalization of culturally specific definitions of "normal" behavior through Western-trained datasets, and the emergence of technological dependency on foreign-controlled AI infrastructure. Rather than rejecting AI-assisted diagnosis, this paper argues that equitable implementation requires culturally representative datasets, participatory model development, and greater local ownership of digital health technologies to ensure that AI promotes both diagnostic accessibility and global health equity.