Fairness-Aware Multi-Task Learning for Age-Gender Prediction with Demographic Parity Constraints

Abstract

Accurate forecasting of gender and age from facial image recognition is a critical mission benchmarked in person-machine interface, security, and personalized services. Usually, older-style single-task models may fail to draw in any manner on the relations between the attributes of age and gender, falsely predicting outcomes across demographic groups. This work then envisages the two new fairness-aware hybrid models for multi-task learning, DP-FairHybrid-MTL and EquiAgeGen-HybridNet. These models will bring together convolutional neural networks, attention mechanisms, and integrate the whole-scale facial root and local feature-extracting characteristics whilst ensuring demographic parity. As observed by way of benchmarking on facial data-sets, DP-FairHybrid-MTL logs a 94.8% gender-accuracy rate on the gender F1-score, 0.946, and on the mean absolute error, MAE=3.21 years of age, beneficially distinguishing the MT and ST baselines. The further improvements, performance-wise, will go to EquiAgeGen-HybridNet, securing 95.6% for gender-accurate, 3.05 years on the MAE, and an F1-score of 0.952, while consistently maintaining parallel predictions for demographic groups. These results stress that fairness-aware hybrid multitask learning improvements with predictability are influential, due to the fact that its mixed methods help tackle the bringing together of problems related to bias and privilege, grounding a reliable and robust frame for ethically founded and highly validated social analysis on faces.