Decision-making systems increasingly orchestrate our world: how to intervene\non the algorithmic components to build fair and equitable systems is therefore\na question of utmost importance; one that is substantially complicated by the\ncontext-dependent nature of fairness and discrimination. Modern decision-making\nsystems that involve allocating resources or information to people (e.g.,\nschool choice, advertising) incorporate machine-learned predictions in their\npipelines, raising concerns about potential strategic behavior or constrained\nallocation, concerns usually tackled in the context of mechanism design.\nAlthough both machine learning and mechanism design have developed frameworks\nfor addressing issues of fairness and equity, in some complex decision-making\nsystems, neither framework is individually sufficient. In this paper, we\ndevelop the position that building fair decision-making systems requires\novercoming these limitations which, we argue, are inherent to each field. Our\nultimate objective is to build an encompassing framework that cohesively\nbridges the individual frameworks of mechanism design and machine learning. We\nbegin to lay the ground work towards this goal by comparing the perspective\neach discipline takes on fair decision-making, teasing out the lessons each\nfield has taught and can teach the other, and highlighting application domains\nthat require a strong collaboration between these disciplines.\n