Abstract
The artificial intelligence industry has been essential in creating new jobs for the deployment of real-world solutions. As a result, the implementation of these new jobs involves the execution of multiple human intelligence micro-tasks, such as data labeling tasks for training machine learning models. The workers who perform those tasks, also known as crowd workers, usually are independent workers within crowdsourcing platforms. These platforms are subject to the free market, where the forces of supply and demand produce various power dynamics among stakeholders. As a result, disassociation between stakeholders often generates unbalanced power dynamics where workers are paid below minimum wage and are intimidated to keep their reputation or face termination. Within this chapter, we introduce computational techniques to audit the workplace conditions of crowd workers and design tools to address these power imbalances, as a positive and more efficient alternative for the labor conditions of crowd workers. This chapter develops these objectives through the design and evaluation of three tools in digital labor platforms: “Invisible Labor Tracker,” “Reputation Agent,” and “CultureFit,” which we describe below. We will demonstrate the sustainability of systems that point to a future where AI can be used to audit and address power imbalances in the workplace.
Cite this work
Carlos Toxtli-Hernández and Saiph Savage. 2023. Designing AI Tools to Address Power Imbalances in Digital Labor Platforms. Torn Many Ways. https://doi.org/10.1007/978-3-031-31642-5_9
@inbook{Toxtli_2023,
title = {Designing AI Tools to Address Power Imbalances in Digital Labor Platforms},
isbn = {9783031316425},
issn = {2524-4477},
url = {http://dx.doi.org/10.1007/978-3-031-31642-5_9},
doi = {10.1007/978-3-031-31642-5_9},
booktitle = {Torn Many Ways},
publisher = {Springer International Publishing},
author = {Toxtli, Carlos and Savage, Saiph},
year = {2023},
pages = {121--137}
}Related
