LIVING IN THE FEED: IMPACT OF ALGORITHM-DRIVEN BEAUTY IDEALS ON INDIAN FEMALE USERS AND THEIR PERCEPTION OF FACIAL DIVERSITY

In the rapidly expanding digital landscape of India, visually driven social media platforms utilize engagement-based algorithms that curate personalized content feeds, profoundly influencing user perception. This paper examines the influence of algorithm-driven beauty ideals on female users in India and their resulting perceptions of facial diversity. The study addresses a critical gap in existing literature by shifting focus from the impact of idealized images to the impact of algorithmic homogeneity—the repeated exposure to a singular, mathematically optimized “preferred face.” The research is grounded in key theoretical frameworks: Social Comparison Theory, which posits that users engage in “upward comparison” against unattainable algorithmic standards; Cultivation Theory, which suggests that heavy exposure to homogenous content distorts users’ perceptions of reality, normalizing artificial features as the “average”; and Post-Colonial Colorism. Drawing on Parameswaran and Cardoza (2009), this latter framework argues that modern algorithmic preferences function as a digital continuation of colonial hierarchies, commodifying “whiteness” and erasing the vast indigenous phenotypic diversity of the Indian population in favor of a singular “cyber-ethnicity.” Methodologically, the study employs a primary quantitative survey of female social media users in India (N=36) to gauge algorithmic awareness and self-perception. The findings reveal that a significant majority (69.4%) perceive faces in their feed as “similar,” with 83.3% attributing this to algorithmic prioritization of specific features. Contrary to the hypothesis of passive consumption, the results uncover a significant “backlash effect,” where nearly half of the respondents (44.4%) reported valuing natural faces more as a reaction to the saturation of artificial uniformity. The paper concludes that while algorithms function as agents of cultural erasure, Indian users are exhibiting a complex form of resistance, navigating the tension between the mathematical push for sameness and the human desire for authenticity.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-15
DOI
https://doi.org/10.5281/zenodo.17777817
Primary Topic
Ethics and Social Impacts of AI
Type
article
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article

LIVING IN THE FEED: IMPACT OF ALGORITHM-DRIVEN BEAUTY IDEALS ON INDIAN FEMALE USERS AND THEIR PERCEPTION OF FACIAL DIVERSITY

Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

LIVING IN THE FEED: IMPACT OF ALGORITHM-DRIVEN BEAUTY IDEALS ON INDIAN FEMALE USERS AND THEIR PERCEPTION OF FACIAL DIVERSITY

article en

Abstract

In the rapidly expanding digital landscape of India, visually driven social media platforms utilize engagement-based algorithms that curate personalized content feeds, profoundly influencing user perception. This paper examines the influence of algorithm-driven beauty ideals on female users in India and their resulting perceptions of facial diversity. The study addresses a critical gap in existing literature by shifting focus from the impact of idealized images to the impact of algorithmic homogeneity—the repeated exposure to a singular, mathematically optimized “preferred face.” The research is grounded in key theoretical frameworks: Social Comparison Theory, which posits that users engage in “upward comparison” against unattainable algorithmic standards; Cultivation Theory, which suggests that heavy exposure to homogenous content distorts users’ perceptions of reality, normalizing artificial features as the “average”; and Post-Colonial Colorism. Drawing on Parameswaran and Cardoza (2009), this latter framework argues that modern algorithmic preferences function as a digital continuation of colonial hierarchies, commodifying “whiteness” and erasing the vast indigenous phenotypic diversity of the Indian population in favor of a singular “cyber-ethnicity.” Methodologically, the study employs a primary quantitative survey of female social media users in India (N=36) to gauge algorithmic awareness and self-perception. The findings reveal that a significant majority (69.4%) perceive faces in their feed as “similar,” with 83.3% attributing this to algorithmic prioritization of specific features. Contrary to the hypothesis of passive consumption, the results uncover a significant “backlash effect,” where nearly half of the respondents (44.4%) reported valuing natural faces more as a reaction to the saturation of artificial uniformity. The paper concludes that while algorithms function as agents of cultural erasure, Indian users are exhibiting a complex form of resistance, navigating the tension between the mathematical push for sameness and the human desire for authenticity.

Zenodo (CERN European Organization for Nuclear Research)
Sri Venkateswara University (IN)
Gender equality
Openalex Percentile: Top 37%
Ethics and Social Impacts of AI
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