Topics of interest include, but are not limited to:

Measuring trust

  • Information needs of crowd workers, requesters and platforms
  • Relevant data sources to foster trust among workers and requesters
  • Extraction of explicit and implicit information from crowdsourcing data sources
  • Subjective and behavioral assessments of trust (i.e., measures)


Building and reinforcing trust

  • Relation between transparency, reputation and trust
  • Trust in traditional workplace vs. the crowd “workplace”: similarities and differences
  • Reputation in crowd work vs. reputation in other online systems (e.g., Airbnb)
  • Monitoring reputation dynamics (e.g., preventing gaming of reputation mechanisms)
  • New automatic and hybrid methods to build reputation out of available and reusable online data
  • Identifying weaknesses and improving, e.g., qualifications
  • Privacy concerns: What information may be public and which may be private? Who decides?
  • Boosting direct communication to clarify and enhance reputation
  • Incentives for opening crowdsourcing platform data
  • Infrastructures for achieving transparency
  • Socioeconomic consequences of crowd work reputation in digital labor platforms


Making use of reputational and other trust-related data

  • Methods for multi-criteria work and people matchmaking
  • Reputation-based crowd recruitment
  • Cross-platform user authentication and data provenance verification methods
  • Ethical aspects
  • New interaction patterns (worker-worker, requester-requester, worker-requester, worker-platform owner and requester-platform owner interaction)
  • Reputation management in new models for platform governance (e.g., cooperative platforms)
  • Trust by design in new crowdsourcing platforms

We welcome discussions combining ideas from the following disciplines:

  • Web Science
  • Computer-Supported Cooperative Work
  • Organizational and Social Psychology
  • Economics
  • Social Science