Bibliometric Analysis of Machine Learning Ethics

Abstract

automating decision-making on a wide range of aspects are affecting humanity. There is great promise in machine learning (ML) and artificial intelligence (AI) algorithms for assisting with predicted and automated decision-making and transforming both societies and industries to improve African citizens' everyday lives. In a business context, platforms such as cloud computing can benefit a lot from AI and ML. Prediction models could be developed to predict the traffic flow of cloud computing resources, which could ease traffic flow. Although AI and ML cloud computing are powerful and vital, they are unfortunately not immune from ethical challenges. As well as improving the allocation of cloud computing resources with prediction models. In recent years, despite AI’s powerful performance, in order to conduct responsible AI and ML modeling, it is important not to ignore ethical issues that might need serious investigation, particularly if human and classified data are concerned. In this regard, the objective of this paper is to determine the prevalence of AI and ML ethics research in Africa. In this paper, RStudio and the computing-related databases (IEEE Xplore, Web of service and Scopus) were used to conduct a bibliometric analysis on the prevalence of AI and ML ethics that relate to the cloud computing environment. The scope was minimized due to its prevalence in the African context. The findings show that AI and ML ethics are understudied on the African continent compared to the West. In this regard, there is a need to conduct more AI and ML ethics-related research in Africa because failure to do so will result in the West shaping the ethical discussion in areas of importance for African development, such as cloud computing. The latter will allow the West to slowly impose its perspective without considering African perspectives. Of note, this paper contributes to the doctoral study with an objective phrased as "to conduct bibliometric analysis to identify the prevalence of cloud computing resource management and traffic flow prediction research in Africa".

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