用数字孪生技术让人工众包更高效真实
Redefining Research Crowdsourcing: Incorporating Human Feedback with LLM-Powered Digital Twins
- 为每位工人创建个性化AI模型，模拟其行为偏好
- 实验显示提升效率并减轻决策疲劳，质量不变
- 适合关注数据真实性与人机协作的研究者
Amazon Mechanical Turk和Prolific等众包平台对研究至关重要，但工人日益使用生成式AI工具带来挑战：研究数据有效性下降，工人角色被边缘化。为此，我们提出一种混合框架，利用数字孪生——个性化AI模型，在保持人类参与的前提下模拟工人的行为与偏好。通过88名众包工人的实验及5名工人、4名社会科学研究者的深度访谈，结果表明数字孪生可提升生产力、减少决策疲劳，同时维持响应质量。研究人员与工人均强调透明性、伦理数据使用和工人自主权的重要性。通过自动化重复任务，保留人类参与复杂判断，数字孪生或可在规模化与真实性间取得平衡。
原文摘要 · Abstract (English)
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.
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