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

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Universal Access in Human-Computer Interaction. User and Context Diversity (HCII 2022)

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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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Acknowledgement

This work is supported by the AI-TOP (2020-1-UK01-KA201-079167) and DIVERSASIA (618615-EPP-1-2020-1-UKEPPKA2-CBHEJP) projects funded by the European Commission under the Erasmus+ programme.

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Authors and Affiliations

  1. Department of Computer Science, Nottingham Trent University, NG11 8NS, Nottingham, UK

    Mufti Mahmud, Muhammad Arifur Rahman, David J. Brown, Nicholas Shopland & Andrew Burton

  2. CIRC and MTIF, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud & David J. Brown

  3. IIT, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser & Shamim Al Mamun

  4. Thapar Institute of Engineering and Technology, Patiala, 147004, Punjab, India

    Tanu Wadhera

  5. University of Derby, Kedleston Road, Derby, DE22 1GB, UK

    Thomas Hughes-Roberts

  6. University Mediterranea of Reggio Calabria, 89124, Reggio Calabria RC, Italy

    Cosimo Ieracitano

  7. Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK

    Marzia Hoque Tania

  8. The University of Queensland, St Lucia, QLD, 4072, Australia

    Mohammad Ali Moni

  9. Deakin University, Burwood, VIC, 3125, Australia

    Mohammed Shariful Islam

  10. School of Applied Sciences Physics, Amity University Rajasthan, Jaipur, India

    Kanad Ray

  11. Department of CSE, University of Chittagong, Chittagong, Bangladesh

    M. Shahadat Hossain

Authors
  1. Mufti Mahmud
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  2. M. Shamim Kaiser
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  3. Muhammad Arifur Rahman
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  14. Kanad Ray
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  15. M. Shahadat Hossain
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Correspondence to Mufti Mahmud .

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Editors and Affiliations

  1. Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  2. University of Crete and Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

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Mahmud, M. et al. (2022). Towards Explainable and Privacy-Preserving Artificial Intelligence for Personalisation in Autism Spectrum Disorder. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. User and Context Diversity. HCII 2022. Lecture Notes in Computer Science, vol 13309. Springer, Cham. https://doi.org/10.1007/978-3-031-05039-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-05039-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05038-1

  • Online ISBN: 978-3-031-05039-8

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