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Using Artificial Intelligence, Machine and Deep Learning to Enhance Evidence-Informed Decision Making
  • Ronald Munatsi
Ronald Munatsi
University of Johannesburg

Corresponding Author:[email protected]

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Abstract

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.