Perspectives critiques sur la recherche en intelligence artificielle pour la détection de l’autisme à partir d’images

Titre original en anglais : Critical Perspectives on Artificial Intelligence Research for Image-Based Autism Detection

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Castelán, M. & Dwyer, P. (2026). Critical Perspectives on Artificial Intelligence Research for Image-Based Autism Detection. IEEE Technology and Society Magazine, 45(1), 64-81. https://doi.org/10.1109/mts.2026.3661481

Date de publication: 01/03/2026 Ajout dans AutiHub: 05/07/2026 Type: Article Langue de l’article: Anglais
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Auteurs

Auteur·ices des publications
2
Auteur·ices de la publication identifié·es comme autistes
1 / 2 (50,0 %)
  1. Mario Castelán
  2. Patrick Dwyer *

Résumé

Ces dernières années, les applications possibles de l’intelligence artificielle (IA) dans l’identification des personnes neurodivergentes ont reçu une attention croissante. À titre d’exemple, une base de données publiquement accessible d’images faciales d’enfants hébergée sur Kaggle a été utilisée pour entraîner des modèles d’IA destinés au diagnostic de l’autisme. La base de données comprend environ 3 000 images d’enfants présumés autistes et neurotypiques et a été utilisée dans environ 50 articles sur l’IA au cours des cinq dernières années, totalisant plus de 500 citations. Cependant, les données ont été compilées à l’aide de recherches en ligne sur des sites web consacrés à l’autisme et des réseaux sociaux. Cet article examine de manière critique la validité scientifique d’un tel jeu de données pour déterminer le statut d’autisme d’un enfant, et met en évidence d’importantes lacunes éthiques et méthodologiques dans la construction et l’utilisation de la base de données. Nous soutenons que ces lacunes auraient pu être évitées si les auteurs avaient collaboré avec des membres de la communauté et des experts interdisciplinaires. En l’absence de méthodes rigoureuses, d’un examen éthique approprié et d’une compréhension véritable des populations étudiées, les modèles d’IA peuvent facilement devenir invalides. Cet article appelle à une réévaluation des implications éthiques et de l’intégrité scientifique liées à l’utilisation de jeux de données faiblement validés dans l’IA appliquée à la recherche sur l’autisme.

In recent years, possible applications of artificial intelligence (AI) in identifying neurodivergent people have received increasing attention. As an example, a publicly available database of children’s facial images hosted on Kaggle has been used to train AI models for autism diagnosis. The database includes approximately 3000 images of putatively autistic and neurotypical children and has been used in around 50 AI articles over the past five years, with more than 500 citations in total. However, the data were compiled using online searches on autism websites and social networks. This article critically examines the scientific validity of such a dataset for determining a child’s autism status, and highlights significant ethical and methodological flaws in the database construction and use. We argue that these flaws could have been prevented if the authors had partnered with community members and interdisciplinary experts. Without rigorous methods, appropriate ethical review, and genuine understanding of the populations being studied, AI models can easily become invalid. This article calls for a re-evaluation of the ethical implications and scientific integrity of using weakly validated datasets in AI for autism research.

Bibliographie citée par cette référence

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Vue d’ensemble de l’inclusion dans la bibliographie

Ces indicateurs décrivent la bibliographie citée importée pour cette publication. Les métriques de références citées utilisent le total des références citées comme dénominateur. Les métriques d’auteurices cité·es indiquent si elles utilisent toutes les occurrences d’auteurices cité·es ou seulement les occurrences rattachées à des auteurices déjà intégré·es à la base de données AutiHub. Ils utilisent les rattachements mis en cache entre les auteurices cité·es et les auteurices intégré·es à la base de données AutiHub. Dernier calcul : 16/08/2026 11:30.

Références citées
135
Nombre total de références citées intégrées pour cette publication.
Références citées avec un·e auteur·ice identifié·e comme autiste
24 / 135 (17,8 %)
Occurrences d’auteur·ices cité·es identifié·es comme autistes
78 / 752 (10,4 %)
Parmi les occurrences rattachées à des auteurices intégré·es à la base de données AutiHub : 78 / 206 (37,9 %). Auteurices cité·es distinct·es identifié·es comme autistes : 56 / 670 (8,4 %).
Occurrences citées rattachées à la base AutiHub
206 / 752 (27,4 %)
Auteurices cité·es distinct·es rattaché·es : 164 / 670 (24,5 %)
Occurrences rattachées, non identifiées comme autistes
128 / 206 (62,1 %)
Parmi les seules occurrences rattachées. Sur l’ensemble des occurrences d’auteurices cité·es : 128 / 752 (17,0 %). Auteurices cité·es distinct·es rattaché·es, non identifié·es comme autistes : 108 / 164 (65,9 %).
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