This thesis seeks to investigate the use of Artificial Intelligence when reviewing STEM job applications and the human biases that are present in AI system training datasets. Further, it proposes to gender neutralize training dataset terms to evaluate job applications based on merit and qualifications, promoting the inclusivity of women in STEM jobs and seeking to eliminate job application system bias from a Utilitarian perspective.