| description abstract | When designing evidence-based policies and programs, decision-makers must distill key information from a vast and rapidly growing literature base. Identifying relevant literature from raw search results is time and resource intensive, and is often done by manual screening. In this study, we develop an artificial intelligence (AI) agent based on a bidirectional encoder representations from transformers (BERT) model and incorporate it into a human team designing an evidence synthesis product for global development. We explore the effectiveness of the human–AI hybrid team in accelerating the evidence synthesis process. To further improve team efficiency, we enhance the human–AI hybrid team through active learning (AL). Specifically, we explore different sampling strategies, including random sampling, least confidence (LC) sampling, and highest priority (HP) sampling, to study their influence on the collaborative screening process. Results show that incorporating the BERT-based AI agent into the human team can reduce the human screening effort, i.e., the number of documents that humans need to screen, by 68.5% compared to the case of no AI assistance and by 16.8% compared to the industry-standard case of using a frequency-based language model and support vector machine-based classifier for identifying 80% of all relevant documents. When we apply the HP sampling strategy, the human screening effort can be reduced even more: by 78.3% for identifying 80% of all relevant documents compared to no AI assistance. We apply the AL-enhanced human–AI hybrid teaming workflow in the design process of three evidence gap maps for U.S. Agency for International Development and find it to be highly effective. These findings demonstrate how AI can accelerate the development of evidence synthesis products and promote timely evidence-based decision-making in global development. | |
