English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

When designing evidence-based policies and programs, decision-makers mustdistill key information from a vast and rapidly growing literature base.Identifying relevant literature from raw search results is time and resourceintensive, and is often done by manual screening. In this study, we develop anAI agent based on a bidirectional encoder representations from transformers(BERT) model and incorporate it into a human team designing an evidencesynthesis product for global development. We explore the effectiveness of thehuman-AI hybrid team in accelerating the evidence synthesis process. To furtherimprove team efficiency, we enhance the human-AI hybrid team through activelearning (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 canreduce the human screening effort by 68.5% compared to the case of no AIassistance and by 16.8% compared to the case of using a support vector machine(SVM)-based AI agent for identifying 80% of all relevant documents. When weapply the HP sampling strategy for AL, the human screening effort can bereduced even more: by 78.3% for identifying 80% of all relevant documentscompared to no AI assistance. We apply the AL-enhanced human-AI hybrid teamingworkflow in the design process of three evidence gap maps (EGMs) for USAID andfind it to be highly effective. These findings demonstrate how AI canaccelerate the development of evidence synthesis products and promote timelyevidence-based decision making in global development in a human-AI hybridteaming context.

ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development