Better Human Computation Through Principled Voting
Abstract
Designers of human computation systms often face the need to aggregate noisy information provided by multiple people. While voting is often used for this purpose, the choice of voting method is typically not principled. We conduct extensive experiments on Amazon Mechanical Turk to better understand how different voting rules perform in practice. Our empirical conclusions show that noisy human voting can differ from what popular theoretical models would predict. Our short-term goal is to motivate the design of better human computation systems; our long-term goal is to spark an interaction between researchers in (computational) social choice and human computation.
Cite
Text
Mao et al. "Better Human Computation Through Principled Voting." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8460Markdown
[Mao et al. "Better Human Computation Through Principled Voting." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/mao2013aaai-better/) doi:10.1609/AAAI.V27I1.8460BibTeX
@inproceedings{mao2013aaai-better,
title = {{Better Human Computation Through Principled Voting}},
author = {Mao, Andrew and Procaccia, Ariel D. and Chen, Yiling},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2013},
pages = {1142-1148},
doi = {10.1609/AAAI.V27I1.8460},
url = {https://mlanthology.org/aaai/2013/mao2013aaai-better/}
}