Alberto Campos Hernández

Task recommendation algorithms in crowd work platforms can erode worker autonomy through strategies such as amplifying the visibility of undesirable or low-paying jobs to meet client demand. We explore whether transparency can shift these systems toward worker-centered values. We built a simulated crowd work platform comparing an opaque Baseline interface against a Transparent variant with explained recommendations and navigation cues. In a within-subjects lab study capturing an initial thirty-minute interaction (n=40), participants rated the transparent system substantially higher on fairness, trust, empowerment, and usability. Behaviorally, transparency supported more strategic task evaluation and increased recommendation alignment. Our results suggest that transparency, even in the form of lightweight disclosure, can improve trust and experience without altering the underlying algorithm. Therefore, we discuss design implications for worker-centered systems, including explanation scope and deployment trade-offs, while acknowledging that durable empowerment requires pairing transparency with meaningful agency and field validation under real economic constraints.

Self-quantifying technology enables users to evaluate their performance and define strategies for improvement. Workplace technology assists users in identifying activities and patterns that facilitate task completion. Software that measures workplace signals requires access to information from the user interface and peripherals. Current software solutions that track computer activity lack goal orientation and do not share raw data that could aid users and researchers in analyzing behavioral patterns at work. This paper presents Wellbot, an intelligent AI assistant capable of tracking workers' activities and providing personalized insights. The solution employs machine learning models to detect goal-oriented and recreational time. Based on users' goals and recent activities, Wellbot generates recommendations aided by Large Language Models. This work aims to enable tools that assist workers through an improved understanding of their context and goals.