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Summary

Artificial intelligence (AI) can enhance macro-task crowdsourcing by supporting facilitation activities. This study identifies seven AI affordances to improve efficiency and effectiveness in complex problem-solving initiatives.

Keywords:
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Area of Science:

  • Computer Science
  • Social Sciences
  • Human-Computer Interaction

Background:

  • Macro-task crowdsourcing offers potential for complex problem-solving, such as addressing climate change.
  • However, its labor-intensive nature and facilitation complexity limit efficiency and effectiveness.
  • Artificial intelligence (AI) presents an opportunity to overcome these limitations in crowdsourcing facilitation.

Purpose of the Study:

  • To explore and understand the potential of AI in facilitating macro-task crowdsourcing.
  • To develop a theoretical framework using affordance theory to describe AI's role.
  • To identify specific AI affordances and their manifestations in crowdsourcing facilitation.

Main Methods:

  • A two-stage, bottom-up approach was employed.
  • Stage 1 involved a structured analysis of academic literature.
  • Stage 2 included validation through two observed macro-task crowdsourcing initiatives and six expert interviews.

Main Results:

  • Seven AI affordances were identified that support 17 distinct facilitation activities.
  • Specific manifestations illustrating these affordances were detailed.
  • The findings provide a clearer understanding of AI's capabilities in crowdsourcing facilitation.

Conclusions:

  • The study advances scholarly understanding of macro-task crowdsourcing and AI's role in facilitation.
  • Practitioners can leverage these findings to integrate AI into crowdsourcing processes.
  • The identified AI affordances have the potential to significantly improve facilitation efficiency and crowdsourcing effectiveness.