Adopting AI teammates in knowledge-intensive crowdsourcing contests: the roles of transparency and explainability

Abstract

Purpose

As the role of AI on human teams shifts from a tool to a teammate, the implementation of AI teammates into knowledge-intensive crowdsourcing (KI-C) contest teams represents a forward-thinking and feasible solution to improve team performance. Since contest teams are characterized by virtuality, temporality, competitiveness, and skill diversity, the human-AI interaction mechanism underlying conventional teams is no longer applicable. This study empirically analyzes the effects of AI teammate attributes on human team members’ willingness to adopt AI in crowdsourcing contests.

Design/methodology/approach

A questionnaire-based online experiment was designed to perform behavioral data collection. We obtained 206 valid anonymized samples from 28 provinces in China. The Ordinary Least Squares (OLS) model was used to test the proposed hypotheses.

Findings

We find that the transparency and explainability of AI teammates have mediating effects on human team members’ willingness to adopt AI through trust. Due to the different tendencies exhibited by members with regard to three types of cognitive load, nonlinear U-shaped relationships are observed among explainability, cognitive load, and willingness to adopt AI.

Originality/value

We provide design ideas for human-AI team mechanisms in KI-C scenarios, and rationally explain how the U-shaped relationship between AI explainability and cognitive load emerges.