Training Neural Nets to Aggregate Crowdsourced Responses

Alex Gaunt, Diana Borsa UCL, Yoram Bachrach
Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, PMLR R14:296-305, 2016.

Abstract

We propose a new method for aggregating crowdsourced responses, based on a deep neural network. Once trained, the aggregator network gets as inputs the responses of multiple participants to a the same set of questions, and outputs its prediction for the correct response to each question. We empirically evaluate our approach on a dataset of responses to a standard IQ questionnaire, and show it outperforms existing state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR14-gaunt16a, title = {Training Neural Nets to Aggregate Crowdsourced Responses}, author = {Gaunt, Alex and UCL, Diana Borsa and Bachrach, Yoram}, booktitle = {Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence}, pages = {296--305}, year = {2016}, editor = {Ihler, Alexander and Janzing, Dominik}, volume = {R14}, series = {Proceedings of Machine Learning Research}, month = {25--29 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r14/main/assets/gaunt16a/gaunt16a.pdf}, url = {https://proceedings.mlr.press/r14/gaunt16a.html}, abstract = {We propose a new method for aggregating crowdsourced responses, based on a deep neural network. Once trained, the aggregator network gets as inputs the responses of multiple participants to a the same set of questions, and outputs its prediction for the correct response to each question. We empirically evaluate our approach on a dataset of responses to a standard IQ questionnaire, and show it outperforms existing state-of-the-art methods.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Training Neural Nets to Aggregate Crowdsourced Responses %A Alex Gaunt %A Diana Borsa UCL %A Yoram Bachrach %B Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2016 %E Alexander Ihler %E Dominik Janzing %F pmlr-vR14-gaunt16a %I PMLR %P 296--305 %U https://proceedings.mlr.press/r14/gaunt16a.html %V R14 %X We propose a new method for aggregating crowdsourced responses, based on a deep neural network. Once trained, the aggregator network gets as inputs the responses of multiple participants to a the same set of questions, and outputs its prediction for the correct response to each question. We empirically evaluate our approach on a dataset of responses to a standard IQ questionnaire, and show it outperforms existing state-of-the-art methods. %Z Reissued by PMLR on 04 October 2026.
APA
Gaunt, A., UCL, D.B. & Bachrach, Y.. (2016). Training Neural Nets to Aggregate Crowdsourced Responses. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R14:296-305 Available from https://proceedings.mlr.press/r14/gaunt16a.html. Reissued by PMLR on 04 October 2026.

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