Towards AI-Empowered Crowdsourcing

• URL: http://arxiv.org/abs/2212.14676v2
• Date: Tue, 19 Sep 2023 08:45:05 GMT
• Title: Towards AI-Empowered Crowdsourcing
• Authors: Shipeng Wang, Qingzhong Li, Lizhen Cui, Zhongmin Yan, Yonghui Xu, Zhuan Shi, Xinping Min, Zhiqi Shen, and Han Yu
• Abstract summary: We propose a taxonomy which divides AI-Empowered Crowdsourcing into three major areas: task delegation, motivating workers, and quality control. We discuss the limitations and insights, and curate the challenges of doing research in each of these areas to highlight promising future research directions.
• Score: 27.0404686687184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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