Redefining Research Crowdsourcing: Incorporating Human Feedback with LLM-Powered Digital Twins
This addresses data validity issues for researchers and role preservation for workers in crowdsourcing platforms, representing an incremental improvement by integrating AI with human feedback.
The paper tackles the problem of compromised data validity in research crowdsourcing due to workers using generative AI, by proposing a hybrid framework with digital twins to emulate human behavior while keeping humans in the loop. Results from an experiment with 88 crowd workers and interviews suggest that digital twins may enhance productivity and reduce decision fatigue while maintaining response quality.
Crowd work platforms like Amazon Mechanical Turk and Prolific are vital for research, yet workers' growing use of generative AI tools poses challenges. Researchers face compromised data validity as AI responses replace authentic human behavior, while workers risk diminished roles as AI automates tasks. To address this, we propose a hybrid framework using digital twins, personalized AI models that emulate workers' behaviors and preferences while keeping humans in the loop. We evaluate our system with an experiment (n=88 crowd workers) and in-depth interviews with crowd workers (n=5) and social science researchers (n=4). Our results suggest that digital twins may enhance productivity and reduce decision fatigue while maintaining response quality. Both researchers and workers emphasized the importance of transparency, ethical data use, and worker agency. By automating repetitive tasks and preserving human engagement for nuanced ones, digital twins may help balance scalability with authenticity.