Using Identification with AR Face Filters to Predict Explicit and Implicit Gender Bias

Marie Jarrell, Etienne Peillard
October, 2023
PDF Cite Project DOI
Gender-manipulated Snapchat face filters used to examine identification and gender bias.

Abstract

We examined whether identification with gender-manipulated Snapchat face filters is associated with implicit and explicit gender bias. In a controlled study, 119 participants used a no-change, male-presenting, or female-presenting filter and completed measures of implicit association, androcentrism, gender-norm agreement, and embodied identification. The filters did not reliably change implicit gender associations or androcentrism. Identification was, however, associated with explicit agreement with gendered norms, with patterns differing according to participants’ gender and the filter used. The findings show that AR face filters are not neutral self-representations, while cautioning against universal bias-reduction interventions based on short AR transformations.

Type
Conference paper
Publication
2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 49-58
Study model connecting filter gender presentation and identification to implicit association, androcentrism, and gender-norm agreement
What was tested. The study separates the filter's gender presentation from the participant's reported identification, then examines their relationship with implicit association, androcentrism, and explicit gender-norm agreement.

The diagram makes the article’s central point visible: AR self-transformation is not treated as a single intervention. Its effects must be understood in relation to the particular filter, the person using it, and the outcome being measured.

Augmented Reality Embodiment Gender Bias Face Filters Social XR
Marie Jarrell
Marie Jarrell
Postdoc

IMT Atlantique
LabSTICC

Hi, I’m Marie, a researcher, designer, and staunch advocate for all things related to video games, VR simulations, and overall digital interactive experiences. A graduate of Clemson University with a PhD in Human-Centered Computing and two Master degrees in Digital Production Arts and Computer Science. I currently utilize my numerous artistic and scientific skills to build video games and research their impact.

Etienne Peillard
Etienne Peillard
Associate Professor

IMT Atlantique
LS2N
CNRS IRL CROSSING

My research focuses on perception and embodied interaction in Virtual and Augmented Reality, including augmented affordances, body perception, and human-system cooperation in immersive environments.