Mitigating Bias in Scientific Data: A Materials Science Case Study

Hengrui Zhang, Wei Chen, James Rondinelli, Wei Chen

Keywords: data-driven informatics, scientific data, data bias, information entropy, active learning

TL;DR: Bias mitigation in scientific databases through a novel active learning method improves accuracy of data-driven prediction models.

Abstract: Growing scientific data and data-driven informatics drastically promote scientific discovery. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. As an example, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.

Submission Track: Original Research

Submission Number: 2