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AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones.
Elizabeth J Miller1, Ben A Steward1, Zak Witkower2
1School of Medicine and Psychology, Australian National University.
Psychological Science
|November 13, 2023
Summary
White AI faces are perceived as more real than human faces, a phenomenon called AI hyperrealism. This occurs because AI algorithms are trained on biased data, leading to misinterpretations of facial attributes.
Area of Science:
- Cognitive Psychology
- Artificial Intelligence Ethics
- Computer Vision
Background:
- AI-generated faces are increasingly realistic and difficult to distinguish from human faces.
- Current AI face generation algorithms exhibit bias, being disproportionately trained on White faces.
- This bias may lead to AI-generated White faces appearing exceptionally realistic.
Purpose of the Study:
- To investigate the phenomenon of AI hyperrealism, where AI-generated White faces are perceived as more human than actual human faces.
- To explore the role of the Dunning-Kruger effect in the misjudgment of AI-generated faces.
- To identify specific facial attributes contributing to AI hyperrealism and explore their potential for machine learning-based debiasing.
Main Methods:
- Experiment 1 involved 124 adults judging the realism of AI-generated and human White faces, alongside reanalysis of existing data.
- Experiment 2 utilized face-space theory and qualitative participant reports with 610 adults to identify distinguishing facial attributes.
- Machine learning was employed to assess the accuracy of identified attributes in distinguishing AI from human faces.
Main Results:
- White AI faces were more frequently judged as human than actual human faces (AI hyperrealism).
- Participants with the most errors in judging faces exhibited the highest confidence, demonstrating a Dunning-Kruger effect.
- Key facial attributes were identified that, while misinterpreted by humans, allowed for high accuracy in machine learning classification.
Conclusions:
- Psychological theories can enhance the understanding of AI-generated outputs and their perception.
- Identified facial attributes and psychological effects provide a basis for debiasing AI algorithms.
- Findings support the ethical development and deployment of artificial intelligence in face generation.
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