Digital Labor: Challenges, Ethical Insights, and Implications

TL;DR

This paper reviews over 300 studies on digital labor in AI, highlighting key challenges faced by gig workers, analyzing representation issues, and offering insights for stakeholders to improve conditions and future research directions.

Contribution

It provides a comprehensive synthesis of literature on digital gig labor, revealing patterns, governance, and implications for AI ecosystems, with new insights for researchers and policymakers.

Findings

Digital workers face low pay and unfair conditions.

Representation of gig workers is often limited and biased.

The paper identifies key areas for intervention and future research.

Abstract

Digital workers on crowdsourcing platforms (e.g., Amazon Mechanical Turk, Appen, Clickworker, Prolific) play a crucial role in training and improving AI systems, yet they often face low pay, unfair conditions, and a lack of recognition for their contributions. To map these issues in the existing literature of computer science, AI, and related scholarship, we selected over 300 research papers on digital labor published between 2015 and 2024, narrowing them down to 143 on digital gig-labor for a detailed analysis. This analysis provides a broad overview of the key challenges, concerns, and trends in the field. Our synthesis reveals how the persistent patterns of representation and voices of gig workers in digital labor are structured and governed. We offer new insights for researchers, platform designers, and policymakers, helping them better understand the experiences of digital workers and pointing to key areas where interventions and future investigations are promptly needed. By mapping the findings from the past ten years' growth of the domain and possible implications, this paper contributes to a more coherent and critical understanding of digital labor in contemporary and future AI ecosystems.