FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery
Xinyi Xu, Zhaoxuan Wu, Arun Verma, Chuan Sheng Foo, Bryan Kian Hsiang Low
Abstract
Scientific discovery aims to find new patterns and test specific hypotheses by analysing large-scale experimental data. However, various practical limitations (e.g., high experimental costs or the inability to perform some experiments) make it challenging for researchers to collect sufficient experimental data for successful scientific discovery. To this end, we propose a collaborative active learning (CAL) framework that enables researchers to share their experimental data for mutual benefit. Specifically, our proposed coordinated acquisition function sets out to achieve individual rationality and fairness so that everyone can equitably benefit from collaboration. We empirically demonstrate that our method outperforms existing batch active learning ones (adapted to the CAL setting) in terms of both learning performance and fairness on various real-world scientific discovery datasets (biochemistry, material science, and physics).
BibTeX
@InProceedings{pmlr-v206-xu23e,
title = {FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery},
author = {Xu, Xinyi and Wu, Zhaoxuan and Verma, Arun and Foo, Chuan Sheng and Low, Bryan Kian Hsiang},
booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
pages = {4033--4057},
year = {2023},
editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
volume = {206},
series = {Proceedings of Machine Learning Research},
month = {25--27 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v206/xu23e/xu23e.pdf},
url = {https://proceedings.mlr.press/v206/xu23e.html},
abstract = {Scientific discovery aims to find new patterns and test specific hypotheses by analysing large-scale experimental data. However, various practical limitations (e.g., high experimental costs or the inability to perform some experiments) make it challenging for researchers to collect sufficient experimental data for successful scientific discovery. To this end, we propose a collaborative active learning (CAL) framework that enables researchers to share their experimental data for mutual benefit. Specifically, our proposed coordinated acquisition function sets out to achieve individual rationality and fairness so that everyone can equitably benefit from collaboration. We empirically demonstrate that our method outperforms existing batch active learning ones (adapted to the CAL setting) in terms of both learning performance and fairness on various real-world scientific discovery datasets (biochemistry, material science, and physics).}
}