Could you become more credible by being White? Assessing Impact of Race on Credibility with Deepfakes

Authors: Kurtis Haut, Caleb Wohn, Victor Antony, Aidan Goldfarb, Melissa Welsh, Dillanie Sumanthiran, Ji-ze Jang, Md. Rafayet Ali, Ehsan Hoque

Published: 2021-02-16 10:05:11+00:00

AI Summary

This research investigates the impact of perceived race on credibility in video conferencing using deepfakes. By manipulating the race of individuals in videos and still images, the study found that subtly altering a person's appearance to appear White significantly increased their perceived credibility, while similar alterations in still images had negligible effects.

Abstract

Computer mediated conversations (e.g., videoconferencing) is now the new mainstream media. How would credibility be impacted if one could change their race on the fly in these environments? We propose an approach using Deepfakes and a supporting GAN architecture to isolate visual features and alter racial perception. We then crowd-sourced over 800 survey responses to measure how credibility was influenced by changing the perceived race. We evaluate the effect of showing a still image of a Black person versus a still image of a White person using the same audio clip for each survey. We also test the effect of showing either an original video or an altered video where the appearance of the person in the original video is modified to appear more White. We measure credibility as the percent of participant responses who believed the speaker was telling the truth. We found that changing the race of a person in a static image has negligible impact on credibility. However, the same manipulation of race on a video increases credibility significantly (61% to 73% with p $<$ 0.05). Furthermore, a VADER sentiment analysis over the free response survey questions reveals that more positive sentiment is used to justify the credibility of a White individual in a video.