Training AI Models for Aesthetic Facial Evaluation: Focused Review and Framework to Mitigate Homogenizing Bias
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By
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Anisha R Kumar
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Lav R Varshney
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June 15, 2026
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Objective:
To review AI training methodologies for aesthetic evaluation, examine sources of bias in training, and evaluate current practices for mitigating bias.
Approach:
Key Findings:
- Distinctiveness negatively affects attractiveness perception universally, while femininity positively influences attractiveness assessments of female faces.
- Facial symmetry and masculinity do not consistently influence attractiveness judgments.
- Cultural preferences significantly modulate aesthetic judgments, indicating the need for culturally diverse AI models.
- AI models trained on biased datasets risk perpetuating narrow beauty ideals and eliminating distinctive ethnic characteristics.
Interpretation:
Limitations:
- Current AI models may not adequately represent diverse patient populations.
- Existing training datasets may be limited in size and diversity.
Conclusion:
