Gender bias in Artificial Intelligence (AI) has been a concern as AI systems are increasingly employed in real-life applications. Despite efforts to mitigate bias, challenges remain in addressing gender bias embedded in machine learning systems, particularly in automated feature extraction processes. This paper examines the presence and impacts of gender bias in AI within the domain of automated feature extraction in computer vision, focusing on online video advertisements, which inherently reflect societal stereotypes. We highlight the limitations of existing mitigation techniques, emphasizing the need for transparency, comparability, and explainability in addressing bias. By systematically analyzing feature extraction methods and their normative harms, we propose a framework for evaluating gender bias by transforming video data into quantifiable features using pre-trained models and analyzing these features through various dimensions grounded in psychology and marketing research. We will employ a multistage approach including video annotation, automated feature extraction, unsupervised learning techniques, and supervised training models. This work provides actionable insights for reducing gender bias and enhancing fairness in AI systems.