Georgetown Law
Technology Review

The gig economy continues to occupy a measurable, though methodologically complex, segment of the U.S. labor market. According to the Report on the Economic Well-Being of U.S. Households in 2024 published by the Federal Reserve, 13% of individuals reported earning income from selling goods, and 9% reported earning income from short-term tasks such as ridesharing, food delivery, or other app-mediated services. These figures reflect the normalization of platform-facilitated supplemental income and underscore the growing role of digital intermediaries in shaping household financial strategies.

At the center of this shift is a governance structure commonly described as algorithmic management. This Article examines how algorithmic management structures work within the gig economy. Section II clarifies the foundational concepts of algorithmic management and the gig economy, emphasizing how both terms function as analytical constructs rather than formally codified legal categories. Section III explains the operational architecture of algorithmic management systems by modeling them as a layered decision structure consisting of data, model, decision, and feedback components. Finally, Section IV applies this framework to Uber’s ride-hailing platform to illustrate how these systems operate in practice and how they shape worker outcomes, procedural safeguards, and regulatory debates. Together, these sections demonstrate that the defining feature of platform labor markets is not merely flexible work, but the relocation of managerial authority from human supervisors to algorithmic systems.