IA, autisme et architecture de la voix : de l'exclusion conçue à la dignité par conception

Titre original en anglais : AI, autism, and the architecture of voice: from engineered exclusion to designed dignity

Srinivasan, H. B. (2026). AI, autism, and the architecture of voice: from engineered exclusion to designed dignity. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03044-3

Date de publication: 18/04/2026 Ajout dans AutiHub: 09/07/2026 Type: Article Langue de l’article: Anglais

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1 / 1 (100,0 %)

Résumé

Résumé Cet article conceptualise l'exclusion conçue — la mise à l'écart prévisible des utilisateurs handicapés résultant de choix relatifs à la provenance des données, aux objectifs des modèles et aux pratiques d'évaluation au sein des systèmes d'IA. Nous examinons l'exclusion conçue à travers les expériences vécues de personnes autistes minimalement verbales et non parlantes, dont les profils de communication remettent en question les paramètres par défaut centrés sur la parole intégrés aux pipelines d'IA contemporains. Ici, la « voix » désigne non seulement la parole, mais aussi l'architecture plus large par laquelle la communication incarnée et multimodale — couvrant le texte de CAA, les gestes, les mouvements et les vocalisations partielles — devient lisible au sein des systèmes d'IA. Le terme « non parlant » ne désigne donc pas l'absence de langage, mais un spectre hétérogène dans lequel la communication dépend souvent de l'état, variant selon la fatigue, l'anxiété, la charge sensorielle et les exigences de planification motrice — des formes de variation que les abstractions de conception effacent habituellement. En retraçant les mécanismes d'exclusion dans la reconnaissance vocale, les systèmes de synthèse vocale, les systèmes de langage clair et la conception d'interfaces, nous introduisons des métriques mesurables de dignité par conception pour l'évaluation technique (Tableau 1) et un cadre de gouvernance cartographiant la responsabilité tout au long du cycle de vie de l'IA (Tableau 2). Nous soutenons que l'accessibilité doit être traitée comme une dimension fondamentale de l'éthique de l'IA — au même titre que l'équité, la vie privée et la sécurité. Réingénierie de l'IA pour la dignité par conception exige des systèmes qui reconnaissent les formes incarnées, multimodales et fluctuantes de communication, élargissant ce qui est considéré comme un signal valide et une innovation responsable.

Abstract This paper conceptualizes engineered exclusion —the predictable sidelining of disabled users resulting from choices about data provenance, model objectives, and evaluation practices within AI systems. We examine engineered exclusion through the lived experiences of minimally and nonspeaking autistic people whose communicative profiles challenge the speech-centered defaults embedded in contemporary AI pipelines. Here, “voice” refers not only to speech but to the broader architecture through which embodied, multimodal communication—spanning AAC text, gesture, movement, and partial vocalizations—becomes legible within AI systems. “Nonspeaking” is therefore not the absence of language but a heterogeneous spectrum in which communication is often state dependent, varying with fatigue, anxiety, sensory load, and motor planning demands—forms of variation that design abstractions routinely erase. Tracing exclusionary mechanisms across speech recognition, text-to-speech, plain-language systems, and interface design, we introduce measurable designed-dignity metrics for technical evaluation (Table 1) and a governance framework mapping accountability across the AI lifecycle (Table 2). We argue that accessibility must be treated as a core dimension of AI ethics—on par with fairness, privacy, and safety. Re-engineering AI for designed dignity requires systems that recognize embodied, multimodal, and fluctuating forms of communication, expanding what counts as valid signal and responsible innovation.

Bibliographie citée par cette référence

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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 11:31.

Références citées
63
Nombre total de références citées intégrées pour cette publication.
Références citées avec un·e auteur·ice identifié·e comme autiste
4 / 63 (6,3 %)
Occurrences d’auteur·ices cité·es identifié·es comme autistes
8 / 157 (5,1 %)
Parmi les occurrences rattachées à des auteurices intégré·es à la base de données AutiHub : 8 / 23 (34,8 %). Auteurices cité·es distinct·es identifié·es comme autistes : 6 / 151 (4,0 %).
Occurrences citées rattachées à la base AutiHub
23 / 157 (14,6 %)
Auteurices cité·es distinct·es rattaché·es : 21 / 151 (13,9 %)
Occurrences rattachées, non identifiées comme autistes
15 / 23 (65,2 %)
Parmi les seules occurrences rattachées. Sur l’ensemble des occurrences d’auteurices cité·es : 15 / 157 (9,6 %). Auteurices cité·es distinct·es rattaché·es, non identifié·es comme autistes : 15 / 21 (71,4 %).
2 entrées de bibliographie citée n’ont pas pu être entièrement enrichies à partir des métadonnées DOI.
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