Hiring is a fundamental, frequent activity for all organizations. Hiring decisions have been reported to be subject to conscious and unconscious biases in the literature. The field of Computational Ethics aims to quantify and maximize the ethicality of decisions. This paper attempts to apply Computational Ethics to the shortlisting process in hiring through the use of Linear Programming. Given a set of applicants for a job with numerical qualification values, the author aims to determine weights for each qualification type to compute scores and resulting rankings for each applicant. To this end, Abstract Moral Theories of Utilitarianism, Maximin/Leximin, Egalitarianism, and Prioritarianism are utilised and applied to a set of randomly generated applicant data. Computational experiments demonstrate that the models are scalable and return interpretable results. The necessity of a quota-based shortlisting system to alleviate disadvantaged candidates is highlighted. The author recommends the use of the Maximin model and iteratively eliminating the applicant with the lowest score.