Recent discussion in the public sphere about classification by algorithms has involved tension between competing notions of what it means for such a classification to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates. In particular, we study how these properties operate when the goal is to rank-order a set of applicants by some criterion of interest, and then to select the top-ranking applicants. Among other results, we show that imposing a constraint to favor "simple" rules -- for example, to promote interpretability -- can have consequences for the equity of the ranking toward disadvantaged groups.