Du chagrin granulaire à la croyance binaire : une optimisation collaborative des techniques d'annotation pour le langage anti-autiste

Titre original en anglais : From Granular Grief to Binary Belief: A Collaborative Optimization of Annotation Techniques for Anti-Autistic Language

Cette publication est incluse dans le projet « Contributions académiques de personnes autistes sur l’autisme ». un auteur·ice de cette publication est identifié·e comme autiste dans le projet.

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Rizvi, N., Flores, A. M., Rizvi, M., Ousidhoum, N., & Munyaka, I. N. S. (2025). From Granular Grief to Binary Belief: A Collaborative Optimization of Annotation Techniques for Anti-Autistic Language. Proceedings of the ACM on Human-Computer Interaction, 9(7), 1-23. https://doi.org/10.1145/3757478

Date de publication: 16/10/2025 Ajout dans AutiHub: 20/10/2026 Type: Article Langue de l’article: Anglais
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Auteurs

Auteur·ices des publications
5
Auteur·ices de la publication identifié·es comme autistes
1 / 5 (20,0 %)
  1. Naba Rizvi *
  2. Alexis Morales Flores
  3. Mohammad Rizvi
  4. Nedjma Ousidhoum
  5. Imani N. S. Munyaka

Résumé

Annoter des textes pour des tâches subjectives, telles que l'étiquetage de textes capacitistes et anti-autistes, constitue un défi qui a suscité une attention considérable, car les paradigmes d'annotation couramment adoptés, par exemple le recours au vote majoritaire, ne parviennent pas à saisir les nuances des annotations de discours haineux ou de biais. L'étiquetage de textes capacitistes et anti-autistes présente des défis similaires, en plus de nécessiter une familiarité avec l'autisme et la discrimination anti-autiste. Dans cet article, nous adoptons une approche collaborative et centrée sur les annotateurs afin d'étudier l'incidence de diverses techniques d'annotation. Nous recrutons 6 participants pour annoter des ensembles de phrases issus de notre corpus de 11 596 phrases. Les groupes annotent selon des schémas axés sur la classification fondée sur des scores, l'étiquetage algorithmique et l'étiquetage fondé sur la comparaison afin d'identifier des cas de discours capacitiste anti-autiste. À la suite de changements dans les schémas d'annotation, nos groupes d'annotateurs passent d'un accord inférieur à celui attendu par hasard à un accord modéré. Cela suggère que la mise en œuvre de discussions de groupe entre annotateurs et la collecte de retours des annotateurs sont susceptibles d'améliorer les scores d'accord dans les tâches difficiles et très subjectives. Nos résultats soulignent l'importance d'une approche collaborative dans les tâches de classification très subjectives, car elle peut conduire à une meilleure compréhension de leurs propres biais et à d'importantes améliorations des scores d'accord, en particulier parmi les annotateurs présentant des taux de désaccord plus élevés. Avertissement : cet article contient des exemples susceptibles d'être offensants ou bouleversants, y compris des insultes explicites employées contre des personnes handicapées.

Annotating text for subjective tasks, such as labeling ableist and anti-autistic texts, is a challenge that has attracted significant attention as commonly adopted annotation paradigms, e.g., using majority voting, fall short in capturing the nuances of hate speech or bias annotations. Labeling ableist and anti-autistic texts presents similar challenges in addition to the need for familiarity with autism and anti-autistic discrimination. In this paper, we adopt a collaborative and annotator-centric approach to study the impact of various annotation techniques. We recruit 6 participants to annotate sets of sentences from our 11,596 sentence corpus. The groups annotate through schemes focused on score-based classification, algorithmic labeling, and comparison-based labeling to identify instances of anti-autistic ableist speech. As a result of changes in annotation schemes, our annotator groups shift from a worse-than-chance agreement to moderate agreement. This suggests that implementing annotator group discussion and collecting annotator feedback is likely to result in improved agreement scores in difficult and highly subjective tasks. Our results highlight the importance of a collaborative approach in highly subjective classification tasks as it may lead to an improved understanding of their own biases, and large improvements in agreement scores, particularly among annotators with higher rates of disagreement. Warning: This paper contains examples that may be offensive or upsetting, including explicit slurs used against people with disabilities.

Bibliographie citée par cette référence

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Vue d’ensemble de la bibliographie citée

Ces indicateurs décrivent la bibliographie citée par cette publication. Un nom d’auteurice est compté chaque fois qu’il apparaît dans une référence citée : une même personne peut donc être comptée plusieurs fois. Les noms qui ne sont pas encore associés à un·e auteurice déjà présent·e dans AutiHub sont traités comme inconnus, pas comme non autistes. Dernier calcul : 28/10/2026 01:01.

Références citées
94
Avec un DOI
49
Sans DOI, à partir du texte brut de la bibliographie
40
7 / 94 (7,4 %) références citées comprennent au moins une personne identifiée comme autiste.
Références avec données à compléter
12 / 94 (12,8 %)
Références avec noms d’auteurices détectés
82 / 94 (87,2 %)
Sans nom d’auteurice détecté
12
Sans titre structuré
12
Sans identifiant stable
40
Références avec noms bruts d’auteurices restant à vérifier
12
Références avec problème de récupération des métadonnées externes
0
Ces indicateurs portent sur les références citées affichées sur cette page, après fusion des doublons techniques. Une référence sans DOI peut quand même soutenir les statistiques d’auteurices lorsqu’un titre et des noms d’auteurices sont disponibles.
Noms d’auteurices détectés dans la bibliographie citée
217
À partir du DOI ou de métadonnées externes
217
À partir du texte brut validé de la bibliographie
0
Noms bruts déjà validés
0
Noms bruts restant à vérifier
12
33 / 217 (15,2 %) noms sont associés à un·e auteurice déjà présent·e dans AutiHub. 184 / 217 (84,8 %) noms ne sont pas encore associés.
Noms associés à une personne identifiée comme autiste
10 / 217 (4,6 %)
Calculé sur l’ensemble des noms d’auteurices détectés dans la bibliographie citée. Parmi les noms associés à un·e auteurice déjà présent·e dans AutiHub : 10 / 33 (30,3 %). Personnes distinctes identifiées comme autistes : 9 / 210 (4,3 %).
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