Artificial Intelligence Approaches to Detecting Labor Market Monopoly in Digital Economy

The rapid expansion of platform-based gig work has sparked growing concerns about algorithm-driven wage suppression and the emergence of labor market monopsony. Particularly in ride-hailing and similar sectors, opaque algorithmic mechanisms enable platforms to subtly control labor supply, task allocation, and compensation, potentially exacerbating income inequalities and weakening worker bargaining power. To identify and characterize these hidden forms of algorithmic monopsony, this paper proposes a novel analytical framework integrating Difference-in-Differences (DID) econometrics with K-medoids clustering. We illustrate our methodology using a synthetic ride-hailing dataset, where DID isolates the average causal impact of an algorithmic policy shift on driver earnings. Subsequently, K-medoids clustering reveals distinct subgroups disproportionately affected, highlighting significant wage reductions among part-time and lower-order drivers. Our findings emphasize the need for enhanced regulatory oversight, mandatory algorithmic transparency, and targeted interventions to protect vulnerable worker segments and ensure equitable labor standards in the digital economy.