2025/06/10 by Leila Namdarian · 1 voice
Social Sciences · #Computational and Text Analysis Methods #Ethics and Social Impacts of AI
paper · doi:10.1080/09537325.2025.2486003
openalex publication_date 2025/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Evidence-based policymaking (EBP) systematically integrates empirical evidence and careful analysis to inform policy decisions. Traditionally, incorporating information into EBP is time-consuming and dependent on human expertise. However, the advent of artificial intelligence (AI) technologies presents an opportunity to revolutionise the collection, analysis, and utilisation of evidence in policymaking. Despite the rapid advancements in AI algorithms, their roles and functions in EBP, particularly in science and technology (S&T), have not been systematically explored. This research aims to examine the functions of AI in S&T EBP, employing an interdisciplinary approach and a non-normative perspective. The study is conducted in two phases. The first phase utilises a comprehensive literature review to identify and conceptualise the components of AI-enabled EBP, including the technological aspects of AI and its policy functions. In the second phase, the constant comparative method (CCM) is employed to categorise and establish relationships between the identified components. This research identifies seven main AI technologies including natural language processing, speech recognition, image recognition and processing, affect detection, autonomous agents, data mining, and artificial creativity, which have 30 policy functions. The proposed framework of this research shows the relationships among AI-enabled EBP steps, AI technologies, and their policy functions.