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

As artificial intelligence (AI) models become increasingly integrated into facial aesthetic surgery for attractiveness prediction and surgical outcome simulation, their potential to perpetuate bias poses clinical concerns. Current models trained on limited datasets inaccurately evaluate underrepresented populations and risk promoting aesthetic homogenization that conflicts with patient goals of ethnic feature preservation. Drawing on current literature, this paper examines bias across AI development stages in aesthetic facial evaluation. Benchmark datasets such as SCUT-FBP (South China University of Technology-Facial Beauty Prediction) and the Chicago Face Database underrepresent older adults, non-White, and ethnically diverse populations. Training methodologies lack fairness-aware techniques, and evaluation focuses on overall rather than demographic-stratified accuracy. While individual mitigation strategies exist-including balanced datasets, adversarial debiasing, and fairness metrics-no comprehensive framework integrates these approaches across the entire development lifecycle. We propose a 6-pillar framework spanning the AI development lifecycle: (1) diverse data collection with synthetic augmentation, (2) fairness-aware training techniques, (3) complementary fairness metrics with intersectional assessment, (4) explainable AI for clinical transparency, (5) stakeholder engagement, and (6) continuous monitoring. Despite the challenges of maintaining algorithmic standardization and cultural specificity, this framework provides implementation guidance for AI developers, clinicians, and institutions, with principles applicable beyond aesthetic surgery to broader facial analysis applications.