Beyond Accessibility: Addressing Industrialized Masking and Cognitive Bias in Neuroinclusive AI Design
Abstract
LLM/AI companions are rapidly transforming human–machine interaction, yet they largely operate under a “one-size-fits-all” paradigm that assumes a neurotypical user. We argue that current AI systems not only fail to accommodate neurodivergent cognitive styles but also may actively enforce “industrialized masking,” an interaction process where neurodivergent users’ authentic communication patterns are suppressed and aligned with normative algorithmic outputs. Drawing on large-scale community data and clinical neuroscience principles, we outline the risks of this cognitive monoculture and propose a framework for an adaptive, neuroinclusive design.
Fields of Study & Subjects
Artificial Intelligence in Healthcare and Education
Neuroethics
Human Enhancement
Biomedical Innovations
AI in Service Interactions
Publication details
- Journal / source
- ACM AI Letters
- Publisher
- Association for Computing Machinery (ACM)
- Volume
- 1
- Issue
- 3
- Pages
- 1–5
- Publication date
- 2 June 2026
- Language
- EN