Automating autism: Disability, discourse, and Artificial Intelligence

As Artificial Intelligence (AI) systems shift to interact with new domains and populations, so does AI ethics: a relatively nascent subdiscipline that frequently concerns itself with questions of “fairness” and “accountability.” This fairness-centred approach has been criticized for (amongst other things) lacking the ability to address discursive, rather than distributional, injustices. In this paper I simultaneously validate these concerns, and work to correct the relative silence of both conventional and critical AI ethicists around disability, by exploring the narratives deployed by AI researchers in discussing and designing systems around autism. Demonstrating that these narratives frequently perpetuate a dangerously dehumanizing model of autistic people, I explore the material consequences this might have. More importantly, I highlight the ways in which discursive harms—particularly discursive harms around dehumanization—are not simply inadequately handled by conventional AI ethics approaches, but actively invisible to them. I urge AI ethicists to critically and immediately begin grappling with the likely consequences of an approach to ethics which focuses on personhood and agency, in a world in which many populations are treated as having neither. I suggest that this issue requires a substantial revisiting of the underlying premises of AI ethics, and point to some possible directions in which researchers and practitioners might look for inspiration

Electroencephalogram (EEG) For Delineating Objective Measure of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child\u27s normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person\u27s ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorithms

Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review

The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed

Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Assessment of the autism spectrum disorder based on machine learning and social visual attention: a systematic review

[EN] The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children¿s social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.The authors have no relevant financial or non-financial interests to disclose. This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive Virtual Environment for the Evaluation and Training of Children with Autism Spectrum Disorder: T Room" (IDI-20170912). This work was also supported by the Spanish Ministry of Science and Innovation funded project "T-EYE: Monitoring system for children with ASD based on artificial intelligence and physiological measures" (IDI-20201146)Minissi, ME.; Chicchi-Giglioli, IA.; Mantovani, F.; Alcañiz Raya, ML. (2021). Assessment of the autism spectrum disorder based on machine learning and social visual attention: a systematic review. Journal of Autism and Developmental Disorders. 1-16. https://doi.org/10.1007/s10803-021-05106-5S116Alcañiz Raya, M., Chicchi Giglioli, I. A., Marín-Morales, J., Higuera-Trujillo, J. L., Olmos, E., Minissi, M. E., Teruel Garcia, G., Sirera, M., & Abad, L. (2020). 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