Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb). Over the years, crowdsourcing has morphed from providing a platform where workers and tasks can be matched up manually into one which leverages data-driven algorithmic management approaches powered by artificial intelligence (AI) to achieve increasingly sophisticated optimization objectives. In this paper, we provide a survey presenting a unique systematic overview on how AI can empower crowdsourcing to improve its efficiency - which we refer to as AI-Empowered Crowdsourcing(AIEC). We propose a taxonomy which divides AIEC into three major areas: 1) task delegation, 2) motivating workers, and 3) quality control, focusing on the major objectives which need to be accomplished. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.

翻译：众包通过动态调动人类智能与生产力，以应对自动化难以独立处理的复杂任务，已发展为一个重要研究领域，并催生了优步（Uber）、爱彼迎（Airbnb）等新型商业模式。多年来，众包已从单纯提供人工匹配任务与工作者的平台，演变为借助人工智能（AI）驱动的数据驱动型算法管理方法，以实现日益复杂的优化目标。本文提出了一项系统性综述，首次系统性地概述了人工智能如何赋能众包以提升其效率——我们将此称为"人工智能赋能众包"（AIEC）。我们提出一种分类体系，将AIEC划分为三大核心领域：1）任务委派、2）工作者激励、3）质量控制，并聚焦于需实现的主要目标。我们讨论了各领域的局限性、洞见及研究挑战，以揭示具有前景的未来研究方向。