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Using deepfakes for experiments in the social sciences - A pilot study.

Andreas Eberl¹, Juliane Kühn¹, Tobias Wolbring¹

¹Chair of Empirical Economic Sociology, Friedrich-Alexander University Erlangen-Nürnberg, Nuremberg, Germany.

Frontiers in Sociology

|December 16, 2022

Summary

Deepfakes, AI-generated media, can be used for controlled social science experiments. A pilot study showed deepfakes successfully manipulated instructor attractiveness, revealing a potential "beauty penalty" in student evaluations.

Keywords:

deep learning deepfakes experiment face swap physical attractiveness student evaluations of teachers

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Social Sciences
Computer Science
Media Studies

Background:

Deepfakes, or AI-generated media, are primarily studied for detection and risks.
Their potential for controlled experimental manipulation in social sciences remains underexplored.

Purpose of the Study:

To investigate deepfakes as a tool for social science research.
To assess the feasibility of creating deepfakes for experimental manipulation.
To examine the impact of perceived physical attractiveness on teacher evaluations.

Main Methods:

Developed a deepfake video altering an instructor's physical attractiveness.
Conducted a pilot study where students rated original and deepfake videos.
Assessed video quality perception and deepfake detection by participants.

Main Results:

Social scientists can create credible deepfakes without specialized expertise.
Participants rated the deepfake and original videos similarly; deepfake was undetected.
Suggestive evidence indicates a "beauty penalty" affecting instructor evaluations.

Conclusions:

Deepfakes offer a viable method for controlled experimental manipulations in social sciences.
The technology facilitates systematic variations while maintaining experimental control.
Ethical considerations for deepfake use in research require careful discussion.