Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction
This addresses ethical concerns in AI by reducing bias in facial attractiveness assessment, which is an incremental improvement over existing methods.
The paper tackled the problem of biased facial beauty prediction by developing AestheticNet, a state-of-the-art network that achieved a Pearson Correlation of 0.9601, outperforming competitors, and proposed a new approach for generating a bias-free CNN to improve fairness.
Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. In the past those results were highly correlated with human ratings, therefore also with their bias in annotating. As artificial intelligence can have racist and discriminatory tendencies, the cause of skews in the data must be identified. Development of training data and AI algorithms that are robust against biased information is a new challenge for scientists. As aesthetic judgement usually is biased, we want to take it one step further and propose an Unbiased Convolutional Neural Network for FBP. While it is possible to create network models that can rate attractiveness of faces on a high level, from an ethical point of view, it is equally important to make sure the model is unbiased. In this work, we introduce AestheticNet, a state-of-the-art attractiveness prediction network, which significantly outperforms competitors with a Pearson Correlation of 0.9601. Additionally, we propose a new approach for generating a bias-free CNN to improve fairness in machine learning.