Using Artificial Intelligence, Machine and Deep Learning to Enhance Evidence-Informed Decision Making

There is pressure for evidence-informed decision-making (EIDM) emanating
from sustainable development challenges, demand for inclusivity,
transparency and accountability. Machine learning (ML) and Deep Learning
(DL) technologies using Artificial intelligence (AI) can enhance EIDM by
easing complex decision-making processes. There is significant evidence
on the effectiveness of these technologies in processing ‘big data’ too
intricate for conventional processing approaches, but a gap exists in
using them to support EIDM. The review draws from a rapid review of
relevant literature to validate the assertion that ‘ML and DL
technologies using AI can enhance EIDM.’ Findings show that originally
computer applications were more practical at transaction processing
levels and less useful in complex decision support systems. Developments
in AI systems changed this. Data management, analytics and visualisation
agencies now exist, enabling evidence integration by applying erudite
analytics - rendering the evidence easily usable by decision-makers
through intuitive visualisation. Complex decision-making can now be
automated using AI, making it possible to analyse data trends, develop
data consistency, forecast, quantify uncertainty, anticipate user
information needs, provide information in the most appropriate form, and
suggest numerous courses of action. It is now feasible to forecast the
effects of future decisions. This way, policymakers can obtain
transformational insights to improve policy outcomes in critical
sectors.