Fairness in crowdwork: Making the human AI supply chain more humane

The vast quantities of data required to build artificial intelligence (AI) technologies are often annotated and processed manually, making human labor a critical component of the AI supply chain. The workers who input this data are sourced through digital labor (“crowdwork”) platforms that often are unregulated and offer low wages, raising concerns about labor standards in AI development. Using the results of a survey, this article aims to shed light on the experiences and perceptions of fair treatment among workers in the AI supply chain. The study reveals significant variability in workers’ experiences, identifies potential drivers of fairness, and highlights how design choices by labor platforms can significantly affect worker welfare. Drawing on lessons from physical supply chains, this article offers practical guidance to managers on how to enhance worker welfare within the AI supply chain and how to ensure that AI technologies are responsibly sourced.

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Publication Details

Journal
Business Horizons
Published
2024-09-21
DOI
https://doi.org/10.1016/j.bushor.2024.09.003
Citations
5
Primary Topic
Digital Economy and Work Transformation
Type
article
Field-Weighted Citation Impact
5.66
Controls
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article

Fairness in crowdwork: Making the human AI supply chain more humane

Catherine Hu, Charles J. Corbett, Auyon Siddiq, Martin Gonzalez-Cabello
5 citations
Business Horizons
Digital Economy and Work Transformation
5.66
article

Fairness in crowdwork: Making the human AI supply chain more humane

Catherine Hu, Charles J. Corbett, Auyon Siddiq, Martin Gonzalez-Cabello
article en
5 citations

Abstract

The vast quantities of data required to build artificial intelligence (AI) technologies are often annotated and processed manually, making human labor a critical component of the AI supply chain. The workers who input this data are sourced through digital labor (“crowdwork”) platforms that often are unregulated and offer low wages, raising concerns about labor standards in AI development. Using the results of a survey, this article aims to shed light on the experiences and perceptions of fair treatment among workers in the AI supply chain. The study reveals significant variability in workers’ experiences, identifies potential drivers of fairness, and highlights how design choices by labor platforms can significantly affect worker welfare. Drawing on lessons from physical supply chains, this article offers practical guidance to managers on how to enhance worker welfare within the AI supply chain and how to ensure that AI technologies are responsibly sourced.

Business HorizonsVol. 68(5)
University of California, Los Angeles (US), Anderson University - South Carolina (US)
Openalex Percentile: Top 4%
Digital Economy and Work Transformation
5.66
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