Ranking-based interfaces are ubiquitous in today's multi-sided online economies (such as online marketplaces, job search, property renting, media streaming). In these systems, the items to be ranked are products, job candidates, creative content, or other entities that transfer economic benefit. It is widely recognized that the position of an item in the ranking has a crucial influence on its exposure which directly translates into economic opportunity. Surprisingly, learning-to-rank (LTR) approaches typically do not consider their impact on the opportunity they provide to the items. Instead, most LTR algorithms solely focus on maximizing the utility of the rankings to the user issuing the query, while there is evidence that this does not necessarily lead to rankings that would be considered fair or desirable in many situations. This thesis proposes a conceptual and computational framework that allows the formulation of fairness constraints on rankings in terms of a merit-based exposure allocation. As a part of this framework, we develop efficient learning-to-rank algorithms that maximize the utility for the user while provably satisfying a specifiable notion of fairness. Since fairness goals can be application-specific, we show that a broad range of fairness constraints can be implemented in this framework using its expressive power to link relevance, merit, exposure, and impact. Beyond the theoretical evidence in deriving the frameworks and algorithms, empirical results on simulated and real-world datasets verify the effectiveness of the approach on both individual and group-fairness notions.