# Towards AI-Empowered Crowdsourcing

arXiv: https://arxiv.org/abs/2212.14676 · HTML: https://mlsanity.com/paper/2212.14676/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 154 references that are in the map.
- First posted on arXiv 28 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [Who Said What: Modeling Individual Labelers Improves Classification](https://mlsanity.com/paper/1703.08774/) (2017)
- [Building Ethics into Artificial Intelligence](https://mlsanity.com/paper/1812.02953/) (2018)
- [Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa](https://mlsanity.com/paper/1803.04223/) (2018)
- [Truth Inference at Scale: A Bayesian Model for Adjudicating Highly Redundant Crowd Annotations](https://mlsanity.com/paper/1902.08918/) (2019)
- [A Survey on Task Assignment in Crowdsourcing](https://mlsanity.com/paper/2111.08501/) (2021)
- [A Technical Survey on Statistical Modelling and Design Methods for Crowdsourcing Quality Control](https://mlsanity.com/paper/1812.02736/) (2018)
- [Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model](https://mlsanity.com/paper/1802.04551/) (2018)
- [Defending against Sybil Devices in Crowdsourced Mapping Services](https://mlsanity.com/paper/1508.00837/) (2015)
- [Federated Crowdsensing: Framework and Challenges](https://mlsanity.com/paper/2011.03208/) (2020)
- [Online Assignment of Heterogeneous Tasks in Crowdsourcing Markets](https://mlsanity.com/paper/1508.03593/) (2015)
- [Exploiting Social Trust Assisted Reciprocity (STAR) towards Utility-Optimal Socially-aware Crowdsensing](https://mlsanity.com/paper/1508.05525/) (2015)
- [FROG: A Fast and Reliable Crowdsourcing Framework (Technical Report)](https://mlsanity.com/paper/1610.08411/) (2016)
- [Task Recommendation in Crowdsourcing Based on Learning Preferences and Reliabilities](https://mlsanity.com/paper/1807.10444/) (2018)
- [Truth Inference on Sparse Crowdsourcing Data with Local Differential Privacy](https://mlsanity.com/paper/1808.08181/) (2018)
- [Ethically Aligned Opportunistic Scheduling for Productive Laziness](https://mlsanity.com/paper/1901.00298/) (2019)
- [A Misreport- and Collusion-Proof Crowdsourcing Mechanism without Quality Verification](https://mlsanity.com/paper/2003.11814/) (2020)
- [A Socially-Aware Incentive Mechanism for Mobile Crowdsensing Service Market](https://mlsanity.com/paper/1711.01050/) (2017)
- [An End-to-End Deep RL Framework for Task Arrangement in Crowdsourcing Platforms](https://mlsanity.com/paper/1911.01030/) (2019)

## Abstract (arXiv)

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.