What is Beautiful is Still Good: The Attractiveness Halo Effect in the era of Beauty Filters

TL;DR

This study investigates how AI beauty filters influence perceptions of attractiveness and traits, revealing a halo effect that is weakened by filters and raising ethical concerns about their use.

Contribution

It provides large-scale empirical evidence of the attractiveness halo effect in digital contexts and explores how beauty filters modify this bias.

Findings

Beauty filters increase attractiveness ratings significantly.

The halo effect weakens with beautified images.

Filters may mitigate cognitive biases.

Abstract

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.