Overcoming Dataset Bias: A Deep Learning Approach to Improve Cross-Cultural Image Classification

Abstract

The difficulties and strategies concerning cross-ethnic image categorization are discussed throughout this report based on deep learning techniques for preventing model bias towards specific ethnic groups. In this part of the project, we continued our discussion of the theory and practice of bias in training datasets and how it impacts the model in the case of emotion recognition and facial analysis using simulations. Three real-time scenarios were explored: public security applications, health status evaluations, education performance, and awareness, all of which must include culturally sensitive data to maximize performance. Some of the issues we defined include the data bias problem, the problem that different cultures exhibit different levels of emotions, and technical concerns such as the limitations of machine learning algorithms, among others that we have described in the paper. Some solutions we provided include data augmentation, real-time learning, and ethical concerns such as using machine learning in making decisions. In doing so, we propose solutions that include building models that are not only precise but also culturally sensitive to avoid prejudice in applications, ranging from credit scoring to complaints handling.