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Diverse misinformation: impacts of human biases on detection of deepfakes on networks

Computer Science

Diverse misinformation: impacts of human biases on detection of deepfakes on networks

J. Lovato, J. St-onge, et al.

This compelling research by Juniper Lovato and colleagues explores human biases in identifying deepfakes, revealing that people perform better when videos align with their own demographics. The study's innovative mathematical model suggests diverse social groups may help shield each other from misinformation. Dive into the findings that could change how we understand video deception!

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Playback language: English
Abstract
This paper investigates how human biases affect the detection of deepfakes, a form of diverse misinformation. An observational survey (N=2016) exposed participants to videos, assessing their ability to identify deepfakes. Results show accuracy varies by demographics, with participants better at classifying videos matching their own demographics. A mathematical model explores population-level impacts, suggesting diverse social groups may offer "diverse correction," where friends protect each other from misinformation.
Publisher
npj Complexity
Published On
Jan 01, 2024
Authors
Juniper Lovato, Jonathan St-Onge, Randall Harp, Gabriela Salazar Lopez, Sean P. Rogers, Ijaz UI Haq, Laurent Hébert-Dufresne, Jeremiah Onaolapo
Tags
deepfakes
human biases
demographics
misinformation
social groups
detection
accuracy

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