NON-CONSENSUAL RHINOPLASTY: MISREPRESENTATION OF FEMALE AQUILINE NOSES IN AI-GENERATED IMAGERY

This article explores how contemporary text-to-image (T21) systems routinely minimise or "correct" aquiline noses in Al-generated images, a phenomenon the authors term "non-consensual rhinoplasty". Despite explicit prompts for pronounced nasal features, many models systematically...

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Bibliographic Details
Published inCommunication Today Vol. 16; no. 1; pp. 90 - 105
Main Authors Kabát, Michal, Kovalèik, Juraj
Format Journal Article
LanguageEnglish
Published Trnava Univerzita sv. Cyrila a Metoda v Trnave 01.04.2025
Subjects
Aesthetics
Algorithms
Artificial intelligence
Beauty
Bias
Content management
Data collection
Females
Nose
Plutarch (46?-120?)
Prejudice
Rhinoplasty
Stereotypes
Online AccessGet full text
ISSN1338-130X

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Summary:This article explores how contemporary text-to-image (T21) systems routinely minimise or "correct" aquiline noses in Al-generated images, a phenomenon the authors term "non-consensual rhinoplasty". Despite explicit prompts for pronounced nasal features, many models systematically smooth out dorsal humps, with 92% of generated images displaying a non-convex profile. Situating these findings in a broader cultural and historical context, the article examines how entrenched beauty standards and physiognomic biases shape both AI training data and societal perceptions. It highlights how content moderation, algorithmic "beautification," and dataset limitations further erase natural variation. To address this bias, the article proposes solutions such as community-led awareness campaigns, petitions for greater transparency in Al development, and technical refinements like prompt sliders for nasal prominence. By outlining these strategies, it advocates for Al innovation that prioritises cultural sensitivity and equitable representation.
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ISSN:1338-130X