AI for proposal handling and Selection

Many aspects of modern peer review have not changed from its inception in the 18th century despite drastic changes in the scientific community. Specifically, contrarily to the early days of peer review, it has become a significant challenge to identify experts that can effectively review the more and more specialized fields of science. The problem is exacerbated by the ever-rising number of researchers (having grown by 15% between 2014 and 2018 according to a UNESCO report) also seen through the staggering increase of publications and proposals (doubling every 14 years in astronomy). Some say that peer review has not adequately innovated as technology has advanced and the dissemination of publications has surged, creating a space for stagnant and biased reviews. We have developed and deployed a novel form of peer review in which the proposers become reviewers themselves known as distributed peer review. We enhanced this process using natural language processing and AI technologies to find an optimal match between the pool of proposals and the reviewers. In this talk, I will present this potential solution that was trialed at ESO and is about to go into full use. I will discuss potential other applications of AI in the field of peer review. I will close with an outlook of current and future experiments in peer review.