  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.
