Research Reproducibility as a Survival Analysis

No. 1: AAAI-21 Technical Tracks 1

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Technical Track on Application Domains

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https://github.com/EdwardRaff/Research-Reproducibility-Survival-Analysis

10.1609/aaai.v35i1.16124