# Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction Source: [Ethically aligned Deep Learning: Unbiased Facial Aesthetic Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:43:15.362Z Authors: Michael Danner, Thomas Weber, Leping Peng, Tobias Gerlach, Xueping Su, Matthias R\\"atsch Published: 2021-11-09T13:53:32.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Facial Beauty Prediction on SCUT-FBP](): Facial Beauty Prediction; SCUT-FBP; PCC: 0.9011. ## Original sources and implementation [Original paper]() [PDF]()