Dynamic Task Allocation Algorithm for Hiring Workers that Learn
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 3825-3830.
Abstract
The automation of hiring decisions is a well-studied topic in crowdsourcing. Existing hiring algorithms make a common assumption — that each worker has a level of task competence that is static and does not vary over time. In this work, we explore the question of how to hire workers who can learn over time. Using a medical time series classification task as a case study, we conducted experiments to show that workers' performance does improve with experience and that it is possible to model and predict their learning rate. Furthermore, we propose a dynamic hiring mechanism that accounts for workers' learning potential. Through both simulation and real-world crowdsourcing data, we show that our hiring procedure can lead to high-accuracy outcomes at lower cost compared to other mechanisms.
@inproceedings{ijcai2016p538,
title = {Dynamic Task Allocation Algorithm for Hiring Workers that Learn},
author = {Pan, Shengying and Larson, Kate and Bradshaw, Josh and Law, Edith},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {3825--3830},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/538},
}
TY - CPAPER TI - Dynamic Task Allocation Algorithm for Hiring Workers that Learn AU - Pan, Shengying AU - Larson, Kate AU - Bradshaw, Josh AU - Law, Edith T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 3825 EP - 3830 UR - https://ijcai.org/proceedings/2016/538 ER -Copied
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