Abstract
Digital hiring platforms increasingly mediate how workers and firms find each other, and their matching algorithms have become part of the institutional fabric of labor markets. In two-sided hiring marketplaces, decisions are rarely a single pass from application to offer; instead, platforms combine search, ranking, screening, messaging, and interview scheduling into iterative processes that allocate attention and opportunity. Algorithmic fairness in this setting therefore concerns not only whether a model treats similar candidates similarly, but also how platform rules shape exposure, competition, and bargaining across both sides. This paper develops theoretical foundations for fairness in two-sided job matching by framing the marketplace as a coupled socio-technical system with strategic actors, incomplete information, and feedback loops. It distinguishes discrimination that arises from historical measurement error, preference-based sorting, platform design choices, and endogenous responses by employers and job seekers. Building on these distinctions, it analyzes intervention families that aim to reduce discrimination while preserving market functionality, including constrained ranking, exposure-aware matching, calibrated screening, incentive-compatible auditing, and governance mechanisms that control data and objective design. The discussion emphasizes failure modes that are specific to two-sided settings, such as shifting discrimination across stages, attention externalities, and equilibrium effects in which group-level outcomes change even when per-decision constraints hold. The paper concludes by outlining evaluation principles that treat fairness as a system property, integrating distributional parity, individual consistency, and welfare robustness under realistic behavioral adaptation.