Is Deepfake Diversity Real? Analyzing the Diversity of Deepfake Avatars

: Kaate, Ilkka; Salminen, Joni; Al Tamime, Reham; Jung, Soon-gyo; Jansen, Bernard J.

Publisher: Elsevier

: OXFORD

: 2025

: Expert Systems with Applications

: EXPERT SYST APPL

: 269

: 12

: 0957-4174

: 1873-6793

DOI: https://doi.org/10.1016/j.eswa.2025.126382

: https://doi.org/10.1016/j.eswa.2025.126382

: https://research.utu.fi/converis/portal/detail/Publication/485076347

Deepfake technology is increasingly integrated into global mobile and web services when human representation is not feasible or cost-effective. Our analysis of 202 deepfake avatars from three deepfake providers reveals significant demographic disparities with 18 out of 48 possible demographic groups unrepresented. Deepfake avatars' gender distribution was nearly balanced (49.01% male, 50.99% female), but older age groups (Baby Boomers and Silent Generation) were substantially underrepresented by 64.36% and 76.24%, respectively, relative to the average number of all deepfake avatars. Differences in language representation were present in deepfake avatar providers with only 1.06% of global languages covered. The findings indicate that current deepfake technology lacks diversity, primarily favoring young white individuals, neglecting older demographics, Asians, and Middle Eastern populations, with underrepresentation of 40.59% and 52.48%, respectively, relative to the average number of all deepfake avatars. Only 15.27% of deepfake avatars portray any occupational characteristics. Addressing these diversity gaps is crucial for better serving varied user groups and warrants attention from deepfake providers and caution from those using deepfakes.

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