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State and Future Prospects of Artificial Intelligence (AI) in Ghana
Abstract
This paper aims to give a broad scope of the current state of AI in Ghana. The paper highlights the existing institutions leveraging AI technologies, points out some current challenges with regards to AI adoption, and identifies some exciting prospects of AI given the current state of the country. Keywords: AI, Machine Learning, Data Science, Technology.
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The deployment of artificial intelligence (AI) technologies is proliferating on the African continent, but policy responses are still at their early stages. This article provides an overview of the main elements of AI deployment in Africa, AI’s core benefits and challenges in African settings, and AI’s core policy dimensions for the continent. It is argued that for AI to build, rather than undermine, socio-economic inclusion in African settings, policymakers need to be cognisant of the following key dimensions: gender equity, cultural and linguistic diversity, and labour market shifts.
With over a billion people, Africa can be better positioned to surmount its health challenges especially regarding maternal and child health and infectious and non-communicable diseases using digital technology including artificial intelligence (AI). The usage of AI in Africa has only seen a few pilots and test cases in human resource planning, child health, diagnostics and pharmaceuticals. Extant challenges around availability of informative data, legal and policy, costs of development, and lack of infrastructure have stymied progress in different parts of the continent. There is tremendous promise in the possibilities that AI offers in transforming and improving healthcare in low-resource areas like Africa. The existing use cases show that it is a viable tool for tackling health challenges, reducing costs, and improving health access and quality. Rather than mere enthusiasm to try out new methods, an evidence-based approach should be employed in decision-making and implementation of AI in healthcare.
Should you be targeted by police for the crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics guidelines (IEEE, EU, and RIKEN, etc.), we refine three basic moral principles specific to person-based PP. We also derive further requirements from case studies, including debates in Chicago, New Orleans, San Francisco, Tokyo, and cities in China. Instead of rejecting PP programs, we analyse what necessary conditions should be met for using the tool to achieve social good. While acknowledging its risks, we conclude that the person-based PP could be beneficial in community policing, especially when merging into a larger governance framework of the social safety net.
Purpose-Fraud is a global economic menace which threatens the survival of individuals, firms, industries and economies, and the mobile money service is no exception. This paper aims to explore the main causes of fraud in the mobile money services in Ghana and the measures to combat the menace by the key stakeholders connected to mobile money services. The paper is motivated by recent reports of numerous fraudulent transactions on the mobile money platform, and the need to clamp down these nefarious transactions with effective and practical measures to sustain the service. Design/methodology/approach-A thorough review of existing studies on fraud risk relating to mobile money services was done revealing a paucity of literature on the subject. Primary data were gathered using an interview guide to explore the magnitude of the problem based on the views of employees of mobile money operators, mobile money agents, banking supervisors from Bank of Ghana, employees of partnering banks, employees of National Communications Authority and mobile money subscribers. Findings-The study revealed that fraud in mobile money services is caused by weak internal controls and systems, lack of sophisticated information technology tools to detect the menace, inadequate education and training and the poor remuneration of employees. These factors disrupt the growth, and the smooth-running of the services. To curb this menace, a detailed legal code and internal fraud policy should be developed and used by mobile money operators and partner banks. Adequate training for mobile money agents should be encouraged coupled with public awareness campaigns to educate stakeholders especially the mobile money subscribers on the tricks of the fraudsters. Research limitations/implications-With the chosen research methodology and limited sample size, the findings may not reflect the views of all the stakeholders connected to the mobile money services. Therefore, future studies on this subject are entreated to use research methods which embrace larger samples to get more details about this menace. Practical implications-The study will assist in tackling mobile money fraud to sustain the service in the foreseeable future. Originality/value-This paper contributes to scanty literature on fraud relating to the mobile money services by drawing lessons from a middle-income country.
Decision-making processes are increasingly based on intelligence gained from ‘big data’, i.e., extensive but complex datasets. This evolution of analyzing complex data using methods aimed at prediction is also emerging within the field of quantitative criminology. In the context of crime analysis, the large amount of crime data available can be considered an example of big data, which could inform us about current and upcoming crime trends and patterns. A recent development in the analysis of this kind of data is predictive policing, which uses advanced statistical methods to make the most of these data to gain useable new insights and information, allowing police services to predict and anticipate future crime events. This article presents the results of a literature review, supplemented with key informant interviews, to give insight into what predictive policing is, how it can be used and implemented to anticipate crime, and what is known about its effectiveness. It also gives an overview of the currently known applications of predictive policing and their main characteristics.
If AI is to improve lives and reduce inequalities, we must build expertise beyond the present-day centres of innovation, says Moustapha Cisse. If AI is to improve lives and reduce inequalities, we must build expertise beyond the present-day centres of innovation, says Moustapha Cisse.
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