The Biasing Effects of Visual Background on Perceived Facial Trustworthiness

Keres, Ashley; Chartier, Christopher R.

doi:10.24839/2164-8204.jn21.3.170

Cited by 6 publications

(1 citation statement)

References 95 publications

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“…For example, researchers can manipulate a person's face while keeping all other video elements like the audio record and its speed, background, clothing, and hairstyles identical. Influences outside the individual, which also affect their perception (Keres and Chartier, 2016 ), can be kept stable across experimental conditions, minimizing biases in estimates of physical attractiveness effects. Consequently, deepfakes offer a high degree of experimental control and thus appear to be a promising method to identify causal effects in experiments by systematically varying only one factor at a time.…”

Section: Previous Studies Using Deepfakesmentioning

confidence: 99%

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

2022

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The advent of deepfakes - the manipulation of audio records, images and videos based on deep learning techniques - has important implications for science and society. Current studies focus primarily on the detection and dangers of deepfakes. In contrast, less attention is paid to the potential of this technology for substantive research - particularly as an approach for controlled experimental manipulations in the social sciences. In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. To demonstrate some of the potentials and pitfalls of deepfakes, we conducted a pilot study on the effects of physical attractiveness on student evaluations of teachers. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. First, our results show that social scientists without special knowledge in computer science can successfully create a credible deepfake within reasonable time. Student ratings of the quality of the two videos were comparable and students did not detect the deepfake. Second, we use deepfakes to examine a substantive research question: whether there are differences in the ratings of a physically more and a physically less attractive instructor. Our suggestive evidence points toward a beauty penalty. Thus, our study supports the idea that deepfakes can be used to introduce systematic variations into experiments while offering a high degree of experimental control. Finally, we discuss the feasibility of deepfakes as an experimental manipulation and the ethical challenges of using deepfakes in experiments.

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Section: Previous Studies Using Deepfakesmentioning

confidence: 99%