FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery

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).