The focus is on the prevalent issue of bias within facial recognition technology (FRT) algorithms. The research aims to understand the root causes of bias in AI facial recognition algorithms, exploring hypotheses related to imbalanced training data, algorithmic flaws, historical societal biases, and underrepresentation in the technical career field. To investigate this, quantitative analysis of existing databases is employed, focusing on real-world interactions across diverse demographic groups. Statistical tools such as chi-square tests and correlation analysis will be used to assess the significance of observed patterns and correlations, considering factors like race, gender, and age as independent variables. The ultimate goal is to provide insights into the biases present in FRT and propose potential solutions to address this pressing issue, emphasizing the importance of diversity in the development teams behind these technologies.