Facial analysis models are increasingly applied in real-world applications that have significant impact on peoples' lives. However, as literature has shown, models that automatically classify facial attributes might exhibit algorithmic discrimination behavior with respect to protected groups, potentially posing negative impacts on individuals and society. It is therefore critical to develop techniques that can mitigate unintended biases in facial classifiers. Hence, in this work, we introduce a novel learning method that combines both subjective human-based labels and objective annotations based on mathematical definitions of facial traits. Specifically, we generate new objective annotations from two large-scale human-annotated dataset, each capturing a different perspective of the analyzed facial trait. We then propose an ensemble learning method, which combines individual models trained on different types of annotations. We provide an in-depth analysis of the annotation procedure as well as the datasets distribution. Moreover, we empirically demonstrate that, by incorporating label diversity, our method successfully mitigates unintended biases, while maintaining significant accuracy on the downstream tasks.
翻译:然而,正如文献所示,对面部特征进行自动分类的模型可能会表现出对受保护群体有算法歧视的行为,从而有可能对个人和社会造成负面影响。因此,开发能够减少面部分类器中意外偏差的技术至关重要。因此,在这项工作中,我们引入一种新的学习方法,既结合基于面部特征数学定义的主观的人类标签,又结合基于面部特征数学定义的客观说明。具体地说,我们从两个大型人类附加说明的数据集中产生新的客观说明,每个数据集都捕捉对面部特征的分析不同角度。然后,我们提出一种混合学习方法,将经过不同类型说明培训的单个模型结合起来。我们深入分析了批注程序和数据集的分布。此外,我们从经验上证明,通过纳入标签多样性,我们的方法成功地减轻了意外偏差,同时保持了下游任务的重要准确性。