Are white people perceived to be more credible? Using Deepfakes AI to assess the impact of race on credibility

Frederick Douglass c. 1855, 1st edition of The North Star, Dec 3 1847, from The Library of Congress
Self-reported demographic information of 305 participants

The Image condition

Here, each participant was shown a still image of a black and a white person, generated using Cycle Generative Adversarial Networks (CycleGAN), a deep learning algorithms that learns underlying structures of the given data, without knowing the target value, run on the Chicago Face Dataset, and made to listen to an audio clip. The participants were told that the speaker was the person whose image they were shown.

The video condition

Here, each participant was shown an original video of a darker-skinned South Asian speaker with subtle changes made to the face area of the speaker using Deepfacelab, a simple, flexible and extensible face swapping framework that allows precise manipulation of skin tone, hair type, eye color, and facial features.

a) Depicts the image condition. b) Depicts the video condition
Questions for the surveys

Analysis of survey responses

The survey results were analyzed in 3 steps with the goal to assess how race influences perceived credibility of the speaker.

Pre-processing to Identify Perceived Race

Here, all responses where the participant did not ”correctly” identify the race of the speaker were filtered out.

Results for the racial perceptions of the participants from the question “What race do you think the speaker is?

Assessing perceived credibility

Here, all responses where the participants thought the speaker was truthful were taken and separated into groups matching the speaker’s race. Next, a Proportions Z-Test was performed to compare and contrast the difference in proportions of perceived credibility amongst the groups.

Summary of Results: “n” is the number of responses after pre-processing. “Credibility” is the percent of responses that believed the speaker was telling the truth. * indicates p < 0.05

Analyzing Sentiment

Here, all responses to questions explaining the rationale why the participants thought the speaker was truthful or not along with all responses to questions where the participants described the speaker’s characteristics in a few adjectives were taken and VADER Sentiment Analysis was performed on them to associate sentiments with racial perception.

Results from VADER compound sentiment analysis of responses to the question “What made you think the person was lying or telling the truth?”
Results from VADER compound sentiment analysis of responses to the question “Use a few adjectives to describe the characteristics of the speaker?”

“You are not judged by the heights you have risen, but from the depths you have climbed” ~Frederick Douglass

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Abhishek Bais

Abhishek Bais

Seasoned R&D EDA, Data Science Enthusiast, Cultural Explorer