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1	Enhancing Fairness in Facial Recognition: Balancing Datasets and Leveraging AI-Generated Imagery for Bias Mitigation : A Study on Mitigating Ethnic and Gender Bias in Public Surveillance Systems Abbas, Rashad, Tesfagiorgish, William Issac January 2024 (has links) Facial recognition technology has become a ubiquitous tool in security and personal identification. However, the rise of this technology has been accompanied by concerns over inherent biases, particularly regarding ethnic and gender. This thesis examines the extent of these biases by focusing on the influence of dataset imbalances in facial recognition algorithms. We employ a structured methodological approach that integrates AI-generated images to enhance dataset diversity, with the intent to balance representation across ethnics and genders. Using the ResNet and Vgg model, we conducted a series of controlled experiments that compare the performance impacts of balanced versus imbalanced datasets. Our analysis includes the use of confusion matrices and accuracy, precision, recall and F1-score metrics to critically assess the model’s performance. The results demonstrate how tailored augmentation of training datasets can mitigate bias, leading to more equitable outcomes in facial recognition technology. We present our findings with the aim of contributing to the ongoing dialogue regarding AI fairness and propose a framework for future research in the field. Facial Recognition Technology Algorithmic Bias Dataset Imbalance ethnic and Gender Representation AI-Generated Images ResNet Model Vgg model Model Performance Evaluation Confusion Matrices AI Fairness and Data Augmentation Engineering and Technology Teknik och teknologier Computer Sciences Datavetenskap (datalogi) Computer and Information Sciences Data- och informationsvetenskap

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Enhancing Fairness in Facial Recognition: Balancing Datasets and Leveraging AI-Generated Imagery for Bias Mitigation : A Study on Mitigating Ethnic and Gender Bias in Public Surveillance Systems