Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach

TL;DR

This paper presents a machine learning model to predict future star scientists in AI by analyzing early-career features, revealing key factors like publication count and diversity that influence scientific success.

Contribution

The study introduces a novel machine learning approach to identify potential star scientists in AI, emphasizing the importance of early-career features and diversity factors.

Findings

Rising stars exhibit different early-career patterns compared to non-rising stars.

Features such as gender and ethnic diversity significantly influence career development.

Number of articles, group discipline diversity, and centrality are key predictors.

Abstract

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation, and they have a significant influence on the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, or research funding decisions. Using machine learning techniques, this study proposes a model to predict star scientists in the field of artificial intelligence while highlighting features related to their success. Our results confirm that rising stars follow different patterns compared to their non-rising stars counterparts in almost all the early-career features. We also found that certain features such as gender and ethnic diversity play important roles in scientific collaboration and that they can significantly impact an author's career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, group discipline diversity, and weighted degree centrality. The proposed approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers.