Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

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.