Quantitative Analysis
Summary
Quantitative analysis in AI alignment research involves using data-driven approaches to gain insights into emerging trends, knowledge gaps, and potential areas for future research. This methodology provides a more objective view of the field’s development and current state. Bibliometric analysis, for instance, has revealed a significant increase in AI safety research activity since 2015, highlighting the growing importance of this field. Quantitative approaches have also helped identify key open issues, such as explainability and value alignment, with the latter being recognized as the most critical long-term research topic. Additionally, these analyses have uncovered a severe lack of research into concrete AI policies, despite the expected profound impact of AI on society. By employing quantitative methods, researchers can better understand the trajectory of AI alignment research and make informed decisions about where to focus future efforts in shaping humanity’s future.