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:

Source

Based on findings from:

Training AI Models for Aesthetic Facial Evaluation: Focused Review and Framework to Mitigate Homogenizing Bias

Anisha R Kumar, Lav R Varshney. Journal Of Medical Internet Research, 2026.

https://www.jmir.org/2026/1/e95452

Attribution Notice

This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.