Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder

Book chapter


Mufti Mahmud, M. Shamim Kaiser, Muhammad Arifur Rahman, Tanu Wadhera, David J. Brown, Nicholas Shopland, Andrew Burton, Hughes-Roberts, T., Shamim Al Mamun, Cosimo Ieracitano, Marzia Hoque Tania, Mohammad Ali Moni, Mohammed Shariful Islam, Kanad Ray and M. Shahadat Hossain 2022. Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. in: Antona, Margherita and Stephanidis, Constantine (ed.) HCII 2022: Universal Access in Human-Computer Interaction. User and Context Diversity New York Springer. pp. 356-370
AuthorsMufti Mahmud, M. Shamim Kaiser, Muhammad Arifur Rahman, Tanu Wadhera, David J. Brown, Nicholas Shopland, Andrew Burton, Hughes-Roberts, T., Shamim Al Mamun, Cosimo Ieracitano, Marzia Hoque Tania, Mohammad Ali Moni, Mohammed Shariful Islam, Kanad Ray and M. Shahadat Hossain
EditorsAntona, Margherita and Stephanidis, Constantine
Abstract

Autism Spectrum Disorder (ASD) is a growing concern worldwide. To date there are no drugs that can treat ASD, hence the treatments that can be administered are mainly supportive in nature and aim to reduce, as much as possible, the symptoms induced by the disorder. However, diagnosis and related treatments in terms of improving communication, social and behavioural skills are very challenging due to the heterogeneity of the disorder and are amongst the largest barriers in supporting people with ASD. Thanks to the recent development in artificial intelligence (AI) and machine learning (ML) techniques, ASD can now be aimed to be detected at an early age. Also, these novel techniques can facilitate administering personalised treatments including cognitive-behavioural therapies and educational interventions. These systems aim to improve the personalised experience for the people with ASD. Acknowledging the existing challenges, this paper summarises the multitudes of ASD, the advancement of AI and ML-based methods in the detection and support of people with ASD, the progress of explainable AI and federated learning to deliver explainable and privacy-preserving systems targeting ASD. Towards the end, some open challenges are identified and listed.

KeywordsAutism Spectrum Disorder ; behavioural skills; privacy-preserving systems
Page range356-370
Year2022
Book titleHCII 2022: Universal Access in Human-Computer Interaction. User and Context Diversity
PublisherSpringer
Place of publicationNew York
SeriesLecture Notes in Computer Science book series (LNCS,volume 13309)
ISBN9783031050398
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-031-05039-8_26
Web address (URL)https://doi.org/10.1007/978-3-031-05039-8_26
Output statusPublished
Publication dates16 Jun 2022
Publication process dates
Deposited22 Jun 2022
EventInternational Conference on Human-Computer Interaction
Web address (URL) of conference proceedingshttps://link.springer.com/book/10.1007/978-3-031-05039-8
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