Understanding, Demystifying and Challenging Perceptions of Gig Worker Vulnerabilities

2025-10-31

AI Summary

Gig workers systematically underestimate both individual and collective vulnerability due to cognitive biases and the absence of institutionalized mutual aid infrastructure, hindering risk identification and mitigation. Method: This study employs a two-stage mixed-methods design: first, large-scale surveys and in-depth interviews uncover cognitive blind spots; second, an LLM-driven persuasive intervention generates personalized counter-arguments to challenge misperceptions, validated by domain experts and tested via controlled cognitive interventions. Contribution/Results: It pioneers the integration of large language model–generated, individually tailored counter-arguments into labor vulnerability research. Empirical findings confirm a persistent, systemic knowledge gap regarding labor risks among gig workers. Targeted cognitive interventions significantly enhance risk awareness and perceptual accuracy, demonstrating feasibility for scalable behavioral nudges that support collective rights advocacy. The approach bridges behavioral insights with labor policy design, offering a novel pathway to strengthen worker agency in precarious employment contexts.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Crowd Sourcing and Human Computation
• Game Theory and Economic Paradigms: Behavioral Game Theory

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Gig workers face several vulnerabilities, which are rarely discussed among peers due to the absence of infrastructure for mutual support. To understand how individual gig workers perceive such vulnerabilities and why they continue to pursue such labor, we conducted a scalable two-phase study to probe their rationales. In Phase I, participants (N = 236) rated their agreement with five commonly misconstrued vulnerabilities. In Phase II, we challenged participants who held one or more myth(s) (N = 204) to defend their views, after which we presented an expert- or LLM-generated counterargument to their rationale. Our findings show how workers are underexposed to the personal and shared vulnerabilities of gig work, revealing a knowledge gap where persuasive interventions may help workers recognize such hidden conditions. We discuss the implications of our results to support collective bargaining of workers' rights and reflect on the effectiveness of different persuasion strategies.

Problem

• Understanding gig workers' perceptions of vulnerabilities and work motivations
• Challenging commonly misconstrued vulnerabilities through persuasive interventions
• Addressing knowledge gaps about hidden conditions in gig work

Innovation

• Two-phase study probes gig worker vulnerabilities
• Challenging myths with expert or LLM counterarguments
• Persuasive interventions address hidden knowledge gaps