EXPLANATORY ARTIFICIAL INTELLIGENCE IN FACE RECOGNITION TASKS

Face Recognition Technology (FRT) has become a fundamental component across various sectors due to advancements in deep neural networks, increased computational power, and the availability of digital images. Nevertheless, the growing application of FRT is accompanied by social, ethical, and legal concerns, including bias, privacy issues, and the lack of model transparency. Explainable Artificial Intelligence (XAI) provides solutions to improve the interpretability of FRT, thereby enhancing trust, ensuring compliance with regulatory standards, and mitigating systemic biases. This article explores the historical evolution of FRT, contemporary XAI methodologies, practical implementations, and existing limitations. Additionally, it discusses recent trends and future directions in the development of XAI for face recognition, focusing on creating more ethical and transparent biometric systems.