AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework

Paritosh Pramanik, Rabin K. Jana and Indranil Ghosh

Technological Forecasting and Social Change, 2024, vol. 205, issue C

Abstract: Artificial Intelligence (AI) readiness enablers are crucial factors related to businesses in a developed or developing economy. With the help of machine learning (ML) approaches like extreme gradient boosting (XGBoost) regression, explainable artificial intelligence (XAI), and explainable boosting machine (EBM), we identify the enablers influencing AI readiness in business. We select 20 developed and 20 developing nations based on the data available from the World Bank, United Nations, and Stanford-Human-Centered AI data repositories. We initially identified 33 factors influencing AI readiness in business for both sets of countries. The XGBoost regression results imply that the AI readiness of developing countries is marginally more predictable than that of developed nations. The outcome of the XAI indicates that three influential AI readiness factors - scientific research output, internet infrastructure, and public consumption expense are common and behave similarly for both developed and developing countries. The research findings have significant practical implications for monitoring and augmenting the end-to-end AI ecosystem to leverage the true potential of digital transformation. Predictive modeling with adequate interpretation helps to identify the impact of the crucial levers of AI readiness in business.

Keywords: AI readiness; AI enablers; Explainable AI; Machine learning; XGBoost regression (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:205:y:2024:i:c:s0040162524002786

DOI: 10.1016/j.techfore.2024.123482

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