Research & Exploration

At least 1 in 6 people living with one or more neurological conditions, 1 in 7 are neurodivergent (asperger's syndrome, rett syndrome, childhood disintegrative disorder, kanner's syndrome, and pervasive developmental disorder). 1 in 4 adults -- suffers from a diagnosable mental disorder in a given year. Consequently, the introduction of accessibility and inclusive design principles was critical to making workplaces, classrooms, cities and ecosystems accessible for millions of individuals presenting abilities, talents, physical and cognitive patterns that were excluded before.

Though, this emerging work still requires a lot of advancement across the areas of algorithmic research, technology transfer, adoption and frameworks. In particular, until 2019 there were no frameworks focused on research and development targeting artificial intelligence and disability, until 2020 - artificial intelligence and children. At the same, the neurodivergent technology research and deployment presenting the intersectional spectrum brings even more challenges and questions.

To date, the ecosystem of technologies and solutions targeting neurodivergent individuals and/or individuals with similar or related psycho-neurological conditions is complex and sophisticated. It includes AI-driven hiring platforms, social robots in classrooms, smart glasses for emotion recognition, speech recognition apps, eye-tracking, biofeedback, virtual and augmented reality for emerging education, data analytics dashboards, various assistive and tracking devices and other solutions. Criteria and mechanisms behind this ecosystem encompass technology, medicine, social, economic and demographic criteria, bioethics. it includes the type of the spectrum, ability and comorbidity, gender, sensibility, physical and tactile experiences, visual and color experiences, differences in the systems of learning, memorizing, systemizing, empathizing, mechanisms of caregivers, human involvement, safety and privacy.

This complex of criteria becomes the foundation for not only the design research but also transparent and biasless development and deployment of AI systems, which use training data across a variety of criteria, taking into account possible correlation, fluctuation short and long-term effects.

Vision & Objectives