Ensuring Fair Representation of Neurodiverse Stakeholders Within the AI-Learning Ecosystem

Abstract

As education is influenced by AI, learners are required to conform to their directive. For the majority of learners with normal automated nervous systems, this is a great advantage because these systems are trained predominately for them. But the learners who are outliners and are neurodiverse? Neurodiversity comprises up to 30% of the population. Individuals with ADHD, Autism, Dyslexia, hyper-mobility and PTSS are often challenged with prescriptive systems. The IEEE has two tools to ensure neural diverse learners are considered: CertifAIEd Algorithmic Bias Criteria and the standard Algorithmic Bias Consideration considers system bias to maximize productive bias, intentionally. By acknowledging AI systems require specific biases, they can be calibrated to consider neural diverse stakeholders thus AI Learning systems can maximize their benefits to both normal neural and diverse neural stakeholders. This chapter will review the CertifAIEd Algorithmic Bias Criteria and the standard Algorithmic Bias Consideration to demonstrate how neurodiverse learners can be considered.

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