Despite the explosion of scholarship in algorithmic fairness, little is understood about how algorithmic fairness constraints interact with human decisions. In this paper, we present and solve a 2-stage hiring model to understand the interplay between algorithmic fairness constraints and human hiring decisions. We consider a hiring scenario in which a diversity-conscious company seeks to hire an employee from a set of applicants. There are more male than female applicants, but both have the same underlying quality distribution. In the first stage, a screening algorithm screens and shortlists candidates. To improve the gender diversity of the workforce, the algorithm has a gender-parity constraint such that it shortlists an equal number of men and women. In the second stage, an unbiased hiring manager interviews the shortlisted candidates and hires the best candidate based on her assessment. We solve this model analytically and identify 3 key parameters that affect the gender proportion of the hires: (1) the size of the applicant pool, (2) the correlation between the algorithm's and the hiring manager's assessment, and (3) the difference in the screening algorithm's predictive power between female and male candidates.