What is Beautiful is Still Good: The Attractiveness Halo Effect in the era of Beauty Filters
Authors: Gulati, A. , Martinez-Garcia, M. , Fernandez, D., Lozano, MA. , Lepri, B. , Oliver, N.
External link: https://doi.org/10.21203/rs.3.rs-4698268/v1
Publication: International Conference on Computational Social Science; International Conference on Thinking, 2024
DOI: https://doi.org/10.48550/arXiv.2407.11981
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A later version of this work has been published in Royal Society Open Science.
The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images from a diverse set of 462 distinct individuals in two conditions: original and attractive after applying a beauty filter.
Our study reveals that the same individuals receive statistically significantly higher ratings of attractiveness and other traits, such as intelligence and trustworthiness, in the attractive condition. We also study the impact of age, gender, and ethnicity and identify a weakening of the halo effect in the beautified condition, resolving conflicting findings from the literature and suggesting that filters could mitigate this cognitive bias. Finally, our findings raise ethical concerns regarding the use of beauty filters.