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Ethical Artificial Intelligence in Telerehabilitation of Neurodevelopmental Disorders: A Position Paper

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Computational Science and Its Applications – ICCSA 2023 Workshops (ICCSA 2023)

Abstract

Neurodevelopmental disorders are a cluster of mental disorders with neurobiological origins that occur during the development of children and lead to cognitive deficits with possible behavioral and emotional consequences. Intensive and individualized interventions are required to take action on these deficits timely. Recently, telerehabilitation techniques for neurodevelopmental disorders have been implemented by automating the rules to set up the intervention protocol. The use of artificial intelligence algorithms primarily applies to this automation. Although these methods have several advantages, such as automatizing personalization and self-adaptation, ethical implications emerged. In detail, it remains unclear how ethical principles can be applied to these new interventions. The present paper outlines a framework of ethical recommendations for using artificial intelligence in telerehabilitation for children with neurodevelopmental disorders. For this aim, a review of the use of artificial intelligence in adults as users is presented and the European Union requirements for trustworthy artificial intelligence for children are explored. The paper proposes some practical applications of ethical principles for artificial intelligence systems in the telerehabilitation of neurodevelopmental disorders and research strategies in line with the European Union guidance. This review of the ethical implication of artificial intelligence is intended to be an opportunity to improve artificial intelligence telerehabilitation of children with neurodevelopmental disorders.

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Castellani, A., Benassi, M., Balboni, G. (2023). Ethical Artificial Intelligence in Telerehabilitation of Neurodevelopmental Disorders: A Position Paper. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14107. Springer, Cham. https://doi.org/10.1007/978-3-031-37114-1_7

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