Neurodiversity is an observed variation of neurological features identified in humans. The study of neurodiversity starts with the ability to understand and train humans, living things, and computers to be culturally diverse without bias. Bias in any form like algorithms or human activities gives rise to racist sentiments that affect humans. Until today, there are no concise solutions to the challenges associated with neurodiversity. Racism is one of the most unexploited underline challenging factors affecting neurodiversity. This study uses keywords for natural language processing to identify four racist morphologies such as prejudice, discrimination, antagonism, and marginalization, and hashtag for artificial intelligence to extract metadata on neurodiversity. Sixteen selected morphologies associated with neurodiversity have been identified. With the help of natural language processing, each racist morphology is matched with neurodiversity to examine the impact of racism on neurodiversity. Neurodiversity Satisfactory Score (NSS) and Neurodiversity Effort Score (NES) have been developed to measure the impact of racism. The metadata and keywords use the formulae, NE/RM≤4=1, NE/RM≤8=2, NE/RM≤12=3, and NE/RM≤16=4, to generate study statistics, where NE is Neurodiversity Element and RM is Racism Morphology. Recorded NSS and NES of 2.356 and 2.356 respectively, indicate that racism impact in the study is equal but harmful to neurodiversity. The study concludes that racism is dangerous to human health and its systematic factors greatly impact neurodiversity even though the human brain has to resist the racist tendency to maintain a balance between racism and brain diversity. The study recommends that more findings should be carried out to develop more substantial factors associated with neurodiversity.

Neurodiversity Natural Language Processing Artificial Intelligence racism neuroplasticity theory morphology