"You are you and the app. There's nobody else.": Building Worker-Designed Data Institutions within Platform Hegemony

Title of the Paper

‘You are you and the app. There’s nobody else.’: Building Worker-Designed Data Institutions within Platform Hegemony

Bibliographic Information

• Research Domain: Human-Computer Interaction (HCI), Platform Economy, Data Institution Design
• Keywords: Platform Hegemony, Data Asymmetry, Platform Workers, Participatory Design, Data Institutions, Data Governance

Research Background and Problem Statement

• Identified Problems or Challenges: Platform workers (e.g., Uber and Deliveroo drivers) are exploited due to information asymmetry, a relationship exacerbated by algorithmic management. Although fairer design solutions exist, they often require platform cooperation, which is difficult to achieve.
• Significance: The platform economy is a crucial component of contemporary labor economics. Addressing information asymmetry can improve working conditions for platform workers and promote social justice.
• Research Motivation and Related Work: Academic discussions have explored the labor challenges caused by information asymmetry, the drawbacks of algorithmic management, and obstacles to worker solidarity. Previous studies have primarily focused on redesigning platform functionalities rather than creating new social and technical architectures for workers. Additionally, many studies have failed to adequately address resistance stemming from capitalist structures.

Proposed Solution

• Proposed Approach: Using Participatory Design (PD) methods to help platform workers reconstruct and reimagine labor data management structures. The core of the research is enabling workers to design "data institutions" that counteract data asymmetry, i.e., worker-led data-sharing architectures.
• Innovations:
  • Rejecting traditional top-down platform functionality redesigns in favor of worker-driven design participation.
  • Introducing the concept of "grassroots data institutions," supporting diverse, small-scale data trusts and collectives tailored to workers' specific needs.
  • Employing critical theories (e.g., Laclau and Mouffe’s "agonistic pluralism") to guide design, aiming to resist centralized power structures of platforms and capitalism.
• Implementation Steps:
  1. Simplify and present the labor data structures collected by platforms (e.g., Uber) to workers.
  2. Conduct multiple design exercises to enable workers to reclassify data, consider the balance between privacy and functionality, and address potential conflicts or risks.
  3. Encourage workers to envision governance roles and social institutional configurations supporting these data structures.

Research Outcomes

• Specific Findings:
  • Workers proposed diverse and often contradictory visions for data institutions, reflecting highly individualized needs.
  • Workers’ settings for data access permissions were highly subjective, involving multiple social and technical assumptions.
  • Suggested data institutions included collective data usage, governance structures, and collaboration mechanisms based on different groups and priorities.
• Advantages Over Existing Solutions:
  • Does not rely on improvements or cooperation from existing platforms, offering an independent data design solution to mitigate significant information asymmetry.
  • Worker participation ensures designs are more attuned to grassroots needs.
• Experimental or Evaluation Results:
  • Workers demonstrated complex understanding, interpreting data structures and proposing innovative solutions, such as blockchain-based direct connection structures or open information platforms similar to Wikipedia.
  • Various design proposals were categorized into clusters, including "Collective Wiki," "Blockchain," and "New Applications."
  • Additionally, workers engaged in in-depth discussions on data security (e.g., anonymization and data aggregation) and the allocation of permissions.
• Limitations and Future Directions:
  • The study’s sample size was small, limiting the generalizability of conclusions.
  • The roles of potentially significant external stakeholders (e.g., consumers, governments, and legal experts) were not analyzed in detail.
  • Future research could conduct further quantitative studies to expand understanding of worker preferences and the generalizability of solutions. Moreover, grassroots data institution models could be developed and implemented for practical operation.

This paper innovatively contributes to data governance and labor rights within the platform economy by enabling workers to design solutions to counteract information asymmetry. It also provides practical pathways for designing within the capitalist framework.