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

Mufti Mahmud; M. Shamim Kaiser; Muhammad Arifur Rahman; Tanu Wadhera; David J. Brown; Nicholas Shopland; Andrew Burton; Thomas Hughes-Roberts; Shamim Al Mamun; Cosimo Ieracitano; Marzia Hoque Tania; Mohammad Ali Moni; Mohammed Shariful Islam; Kanad Ray; M. Shahadat Hossain

doi:10.1007/978-3-031-05039-8_26

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

Mufti Mahmud^*
, M. Shamim Kaiser
, Muhammad Arifur Rahman
, Tanu Wadhera
, David J. Brown
, Nicholas Shopland
, Andrew Burton
, Thomas Hughes-Roberts
, Shamim Al Mamun
, Cosimo Ieracitano
, Marzia Hoque Tania
, Mohammad Ali Moni
, Mohammed Shariful Islam
, Kanad Ray
, M. Shahadat Hossain

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

69 Scopus citations

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.

Original language	English
Title of host publication	Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings
Editors	Margherita Antona, Constantine Stephanidis
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	356-370
Number of pages	15
ISBN (Print)	9783031050381
DOIs	https://doi.org/10.1007/978-3-031-05039-8_26
State	Published - 2022
Externally published	Yes
Event	16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022 - Virtual, Online Duration: 26 Jun 2022 → 1 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13309 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022
City	Virtual, Online
Period	26/06/22 → 1/07/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Behavioural data
Education
Federated learning
Healthcare data
Multimodal system
Physiological data
Rehabilitation
Self-reports
Wearable devices

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-05039-8_26

Cite this

Mahmud, M., Kaiser, M. S., Rahman, M. A., Wadhera, T., Brown, D. J., Shopland, N., Burton, A., Hughes-Roberts, T., Mamun, S. A., Ieracitano, C., Tania, M. H., Moni, M. A., Islam, M. S., Ray, K., & Hossain, M. S. (2022). Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. In M. Antona, & C. Stephanidis (Eds.), Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings (pp. 356-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13309 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-05039-8_26

Mahmud, Mufti ; Kaiser, M. Shamim ; Rahman, Muhammad Arifur et al. / Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. editor / Margherita Antona ; Constantine Stephanidis. Springer Science and Business Media Deutschland GmbH, 2022. pp. 356-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; Conference date: 26-06-2022 Through 01-07-2022",

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Mahmud, M, Kaiser, MS, Rahman, MA, Wadhera, T, Brown, DJ, Shopland, N, Burton, A, Hughes-Roberts, T, Mamun, SA, Ieracitano, C, Tania, MH, Moni, MA, Islam, MS, Ray, K & Hossain, MS 2022, Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. in M Antona & C Stephanidis (eds), Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13309 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 356-370, 16th International Conference on Universal Access in Human-Computer Interaction, UAHCI 2022 Held as Part of the 24th HCI International Conference, HCII 2022, Virtual, Online, 26/06/22. https://doi.org/10.1007/978-3-031-05039-8_26

Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. / Mahmud, Mufti; Kaiser, M. Shamim; Rahman, Muhammad Arifur et al.
Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. ed. / Margherita Antona; Constantine Stephanidis. Springer Science and Business Media Deutschland GmbH, 2022. p. 356-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13309 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Brown, David J.

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AU - Burton, Andrew

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Mahmud M, Kaiser MS, Rahman MA, Wadhera T, Brown DJ, Shopland N et al. Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. In Antona M, Stephanidis C, editors, Universal Access in Human-Computer Interaction. User and Context Diversity - 16th International Conference, UAHCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 356-370. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-05039-8_26