Gig2Gether: Datasharing to Empower, Unify and Demistify Gig Work

Research Background and Issues

Issues or Challenges

The authors identify several key issues exposed by the current platform-based gig economy, despite its transformative impact on labor models and social mobility:

1. Lack of data transparency: Platforms collect workers' personal work data and use it to optimize algorithms, but these data are inaccessible to workers or policymakers.
2. Social and physical isolation: Gig work environments lead to fragmentation among workers, weakening their collective identity.
3. Lack of structural protections: Including safety concerns, wage inequities, and unregulated platform algorithms.

Importance

The rapid rise of the gig economy has reshaped labor distribution and employment models, but it has also introduced systemic issues related to working conditions. These issues directly affect the financial stability, physical and psychological safety, and collective bargaining power of millions of gig workers. Addressing these challenges urgently requires collaboration between technology and policy, particularly through data sharing to foster collective identity and policymaking.

Research Motivation and Related Work

Although existing research highlights the potential of data-sharing tools to enhance self-reflection, audit platform algorithms, and promote collective solidarity, these tools are often limited to single platforms and lack cross-platform support. Furthermore, most gig workers lack experience in actively contributing personal data to support policy creation. To address these limitations, this paper proposes a novel system—Gig2Gether—to explore how cross-platform data sharing can support workers' multidimensional goals.

Solution

Methods or Solutions

The authors propose a system called Gig2Gether, which provides the following functionalities for gig workers:

1. Data and experience sharing: Workers can upload quantitative or qualitative data (e.g., income, expenses, work experiences) to the platform, enabling cross-platform mutual assistance and collective solidarity.
2. Personal trends and collective insights: The system offers personalized summaries of work trends and aggregated worker data to analyze working conditions from micro to macro levels.
3. Planning and resource connection: It supports workers in forecasting income, planning future work, and connecting to resources such as tax preparation.

Innovations

1. Cross-platform support: Unlike existing tools that typically support single platforms, Gig2Gether supports multiple gig platforms (e.g., Rover, Uber, Upwork), facilitating knowledge and data exchange among workers.
2. Flexible data control: Users can choose the granularity of shared details, data retention duration, and target audience (e.g., other workers, policymakers).
3. Privacy and security: The system employs anonymization and non-identifiable statistical data to reduce privacy risks.

Implementation Steps

1. Design iteration: Optimize system functionality and interface through interactive prototype testing and feedback collection from worker users.
2. Module development: Build modules for income and expense tracking, trend analysis, and story sharing.
3. System testing: Evaluate Gig2Gether's functionality and improvement suggestions through seven days of real-world usage data and interviews with 16 gig workers.

Research Outcomes

Specific Outcomes

Gig2Gether achieved the following outcomes:

1. Workers across multiple platforms used the story-sharing module to share strategies and foster cross-industry solidarity, creating a supportive environment.
2. The data aggregation feature helped workers analyze work patterns and income trends, enabling better financial planning.

Advantages over Existing Solutions

1. Collective solidarity: Provides a social support network among workers, surpassing traditional individual tracking tools.
2. Comprehensiveness: Supports workers' multidimensional needs (e.g., mental health, financial responsibility, and policy participation) through qualitative and quantitative data.
3. Privacy protection and openness: Enables workers to take a proactive role in data contribution through customizable sharing settings.

Experimental or Evaluation Results

1. Over 60% of workers used the "trend view" feature to compare incomes and plan future work.
2. The story-sharing feature was widely appreciated, allowing users to easily highlight strategies or issues in their work and select sharing targets.
3. During the seven-day field test, participants uploaded 120 income records, 20 expense records, and 27 stories.

Limitations and Future Directions

Limitations:

• The current user base is relatively small, covering only three platforms: Uber, Rover, and Upwork.
• Some features are not yet fully automated (e.g., data uploads still require manual input).

Future Directions:

1. Expand platform support to cover more gig sectors.
2. Enhance automated data upload functionality while ensuring privacy and security.
3. Collaborate with policymakers and worker organizations to explore the system's practical applications in policy advancement.

Through the Gig2Gether platform, the authors demonstrate that data sharing can be an effective tool for promoting worker solidarity and policy reform, showcasing the vast potential of combining technology and policy to address challenges in the gig economy.