Using artificial intelligence to enhance evidence informed-decision-making

Background: Sustainable development challenges are pressuring governments worldwide for evidence-informed decision-making (EIDM). The complexity of these challenges necessitates a multi-disciplinary approach to EIDM. Despite evidence of the efficacy of artificial intelligence (AI) in processing big data, there is a gap in their use in enhancing EIDM. Objectives: The study aims to validate the claim that ‘AI can enhance EIDM’. Method: A general systematic review methodology partially using abridged systematic review principles was used to collect and synthesise evidence on the use of AI, Machine Learning (ML) and Deep Learning (DL) in EIDM. Thematic content analysis was conducted to analyse the review data. Results: Despite some equity, validation, interoperability, transparency and other challenges, AI can facilitate evidence synthesis and intuitive visualisation agencies that enable complex analysis for easy comprehension and use in decision-making. AI-based ML and DL can improve EIDM by streamlining complex decision-making procedures and enhancing process efficiency and objectivity. Conclusion: Complex decision-making may now be automated through consistent data trend analysis, forecasting, uncertainty quantification, user demand prediction, choice recommendation and suitable information packaging using AI-driven technologies. Gaining transformational insights to improve decision outcomes in important sectors is now feasible, but more research is required to address fairness and bias issues in AI systems, guarantee openness and explainability, create strong data governance frameworks and encourage citizen engagement. Contribution: This study provides a solid basis for examining a more comprehensive framework tying theory and practice in a way that is understandable and essential to mainstreaming the use of AI in EIDM.