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

2026-09-30

AI Summary

This study addresses the cognitive misalignment between users and delivery riders arising from real-time tracking data on food delivery platforms, which often results in unjust accountability. Employing socio-technical analysis and semi-structured interviews, this work reveals the mechanisms through which tracking data is moralized and uncovers riders’ invisible digital labor. Building on these findings, it proposes a “contextual transparency” design framework that delineates a “burden-shifting” mechanism to transfer explanatory responsibility from individual riders to the platform system. By elucidating the systemic deficiencies inherent in current tracking architectures, this research provides both a theoretical foundation and practical directions for developing more equitable algorithmic governance and human-computer interaction design within platform-mediated labor environments.

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.

Problem

• food delivery platforms
• real-time tracking
• gig workers
• accountability
• socio-technical systems

Innovation

• real-time tracking systems
• socio-technical analysis
• anticipatory data management
• burden-shifting mechanism
• contextual transparency