Towards AI-Empowered Crowdsourcing

TL;DR

This paper surveys how artificial intelligence can enhance crowdsourcing by improving task delegation, worker motivation, and quality control, aiming to boost efficiency and outline future research challenges.

Contribution

It introduces a systematic taxonomy of AI-empowered crowdsourcing, covering key areas and providing insights and challenges for future research.

Findings

AI improves task delegation efficiency

AI enhances worker motivation strategies

AI-based quality control methods are effective

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.