When Data Becomes Judgment: Misaligned Interpretations and Accountability in Food Delivery Platforms

Abstract

Food delivery platforms mediate service encounters through real-time tracking data. While presented as objective, such data often obscures the situational constraints shaping delivery work, producing systematic misalignments between user perception and courier experience. This study presents a socio-technical analysis of real-time mobile tracking systems in the wild. Through semi-structured interviews with 23 users and 17 couriers on Chinese food delivery platforms, we identify two interrelated dynamics. First, users operate within a data-as-behavior interpretive framework, translating spatial and temporal anomalies into moralized judgments of courier negligence. Second, couriers engage in anticipatory data management, a form of hidden digital labor in which they reshape their physical behavior to produce interface-legible trajectories rather than physically optimal ones. Together, these findings expose a burden-shifting mechanism---characterizing the systemic outcomes of decontextualized interface design rather than explicit designer intent---in current tracking architectures, demonstrating how current tracking architectures leave gig workers bearing much of the explanatory burden. We propose design directions toward contextual transparency, redistributing this explanatory burden from individual workers to the platforms that possess the logistical context to bear it.