mlsanityData to 7 Oct 2026

Lookism: The overlooked bias in computer vision

arXiv 2408.11448 ↗First posted 21 Aug 2024cs.CV cs.AI cs.CYcited by 4

Not on the map. Why?

Abstract (arXiv)

In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and generation systems in socially relevant applications, from hiring to security screening. However, the prevalence of biases within these systems has raised significant ethical and social concerns. The most extensively studied biases in this context are related to gender, race and age. Yet, other biases are equally pervasive and harmful, such as lookism, i.e., the preferential treatment of individuals based on their physical appearance. Lookism remains under-explored in computer vision but can have profound implications not only by perpetuating harmful societal stereotypes but also by undermining the fairness and inclusivity of AI technologies. Thus, this paper advocates for the systematic study of lookism as a critical bias in computer vision models. Through a comprehensive review of existing literature, we identify three areas of intersection between lookism and computer vision. We illustrate them by means of examples and a user study. We call for an interdisciplinary approach to address lookism, urging researchers, developers, and policymakers to prioritize the development of equitable computer vision systems that respect and reflect the diversity of human appearances.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 70 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

16 of 70 references.

References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2019Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigationcited by 252
  5. 2023Social Biases through the Text-to-Image Generation Lenscited by 126
  6. 2020Face Recognition: Too Bias, or Not Too Bias?cited by 82
  7. 2022A Systematic Study of Bias Amplificationcited by 65
  8. 2023SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examplescited by 53
  9. 2021Understanding and Mitigating Annotation Bias in Facial Expression Recognitioncited by 50
  10. 2020Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentationcited by 45
  11. 2021One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Visioncited by 33
  12. 2023Investigating Subtler Biases in LLMs: Ageism, Beauty, Institutional, and Nationality Bias in Generative Modelscited by 32
4 more
  1. 2024Uncovering Bias in Large Vision-Language Models at Scale with Counterfactualscited by 27
  2. 2024Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Modelscited by 10
  3. 2019On the effect of age perception biases for real age regressioncited by 5
  4. 2022OpenFilter: A Framework to Democratize Research Access to Social Media AR Filterscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025When Algorithms Play Favorites: Lookism in the Generation and Perception of Facescited by 2
  2. 2026Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classificationcited by 2
  3. 2025Erasing 'Ugly' from the Internet: Propagation of the Beauty Myth in Text-Image Modelscited by 1
  4. 2026Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generationcited by 1