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Diversity is not a one-way street: pilot study on ethical interventions for racial bias in text-to-image systems

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AuteurRechercher : 1; Rechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches Canada. Technologies numériques
Bailleur de fondsRechercher : Conseil national de recherches Canada. Programme de vie privée et sécurité numériques
FormatTexte, Article
Conférence14th International Conference on Computational Creativity (ICCC'23), June 19-23, 2023, Waterloo, Ontario, Canada
Description physique5 p.
Résumé
Text-to-image generation models can reflect the underlying societal biases present in their training data. However, user-level interventions to encourage greater diversity in the output have been proposed. Here, we examine visually stereotypical output from three widely-used models: DALL-E 2, Mid-journey, and Stable Diffusion. Some of the prompts we consider (e.g., “a photo portrait of a lawyer”) result in an underrepresentation of darker-skinned individuals in the output, while other prompts (e.g., “a photo portrait of a felon”) result in over-representation of darker-skinned individuals. We show that existing linguistic interventions serve to correct for under-representation to some degree, but in fact amplify the bias in cases of over-representation for all three systems. Further work is needed to develop effective methods to promote equity, diversity, and inclusion in the output of image generation systems.
Date de publication
Maison d’éditionInternational Conference on Computational Creativity
University of Waterloo
Dans
  • ICCC'23 (23 juin 2023).
Langueanglais
Publications évaluées par des pairsOui
IdentificateurPaper 97
Exporter la noticeExporter en format RIS
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Identificateur de l’enregistrementade03101-9ec3-4e46-a5f9-2ebd5609ccdb
Enregistrement créé2023-09-08
Enregistrement modifié2025-12-18

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