Abstract
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.
Cite this work
Fateme Mazdarani, Alberto Campos Hernández, and Carlos Toxtli-Hernández. 2026. Worker-Centered AI: Transparent Explanations for Trustworthy Task Recommendation in Crowd Work. 2026 IEEE Conference on Artificial Intelligence (CAI). https://doi.org/10.1109/cai68641.2026.11536153
@inproceedings{Mazdarani2026Worker,
title = {Worker-Centered AI: Transparent Explanations for Trustworthy Task Recommendation in Crowd Work},
url = {http://dx.doi.org/10.1109/CAI68641.2026.11536153},
doi = {10.1109/cai68641.2026.11536153},
booktitle = {2026 IEEE Conference on Artificial Intelligence (CAI)},
publisher = {IEEE},
author = {Mazdarani, Fateme and Hernandez, Alberto Campos and Toxtli, Carlos},
year = {2026},
month = May,
pages = {295--302}
}Related
