TY - JOUR
T1 - AI Hyperrealism
T2 - Why AI Faces Are Perceived as More Real Than Human Ones
AU - Miller, Elizabeth J.
AU - Steward, Ben A.
AU - Witkower, Zak
AU - Sutherland, Clare A.M.
AU - Krumhuber, Eva G.
AU - Dawel, Amy
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2023/12
Y1 - 2023/12
N2 - Recent evidence shows that AI-generated faces are now indistinguishable from human faces. However, algorithms are trained disproportionately on White faces, and thus White AI faces may appear especially realistic. In Experiment 1 (N = 124 adults), alongside our reanalysis of previously published data, we showed that White AI faces are judged as human more often than actual human faces—a phenomenon we term AI hyperrealism. Paradoxically, people who made the most errors in this task were the most confident (a Dunning-Kruger effect). In Experiment 2 (N = 610 adults), we used face-space theory and participant qualitative reports to identify key facial attributes that distinguish AI from human faces but were misinterpreted by participants, leading to AI hyperrealism. However, the attributes permitted high accuracy using machine learning. These findings illustrate how psychological theory can inform understanding of AI outputs and provide direction for debiasing AI algorithms, thereby promoting the ethical use of AI.
AB - Recent evidence shows that AI-generated faces are now indistinguishable from human faces. However, algorithms are trained disproportionately on White faces, and thus White AI faces may appear especially realistic. In Experiment 1 (N = 124 adults), alongside our reanalysis of previously published data, we showed that White AI faces are judged as human more often than actual human faces—a phenomenon we term AI hyperrealism. Paradoxically, people who made the most errors in this task were the most confident (a Dunning-Kruger effect). In Experiment 2 (N = 610 adults), we used face-space theory and participant qualitative reports to identify key facial attributes that distinguish AI from human faces but were misinterpreted by participants, leading to AI hyperrealism. However, the attributes permitted high accuracy using machine learning. These findings illustrate how psychological theory can inform understanding of AI outputs and provide direction for debiasing AI algorithms, thereby promoting the ethical use of AI.
KW - StyleGAN2
KW - artificial intelligence
KW - face perception
KW - face-space theory
KW - generative adversarial network
KW - open data
KW - open materials
UR - http://www.scopus.com/inward/record.url?scp=85176916168&partnerID=8YFLogxK
U2 - 10.1177/09567976231207095
DO - 10.1177/09567976231207095
M3 - Article
SN - 0956-7976
VL - 34
SP - 1390
EP - 1403
JO - Psychological Science
JF - Psychological Science
IS - 12
ER -