Fair and Explainable Dynamic Engagement of Crowd Workers

被引：0

作者：

Yu, Han ^{[1
]}

Liu, Yang ^{[2
]}

Wei, Xiguang ^{[2
]}

Zheng, Chuyu ^{[2
]}

Chen, Tianjian ^{[2
]}

Yang, Qiang ^{[2
,3
]}

Peng, Xiong ^{[4
]}

机构：

[1] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore

[2] WeBank, Dept AI, Shenyang, Peoples R China

[3] Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Hong Kong, Peoples R China

[4] Better Life Commercial Chain Share Co Ltd, Xiangtan, Peoples R China

来源：

PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2019年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.

引用

页码：6575 / 6577

页数：3

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