Conference Paper

State and Future Prospects of Artificial Intelligence (AI) in Ghana

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

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.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Full-text available
Article
Full-text available
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.
Article
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.
Google has opened its first Africa Artificial Intelligence lab in Ghana -CNN
  • Adeoye
Adeoye. Google has opened its first Africa Artificial Intelligence lab in Ghana -CNN, Apr. 2019. URL https://edition.cnn.com/2019/04/14/africa/google-ai-center-accra-intl/ index.html.
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network
  • D Akogo
  • V Appiah
  • X.-L Palmer
D. Akogo, V. Appiah, and X.-L. Palmer. CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network, 2018.
Banking Industry Fraud Report -Bank of Ghana
  • Bog
  • The
BOG. The 2019 Banking Industry Fraud Report -Bank of Ghana, Dec. 2020. URL
MTN to deploy AI, others to fight MoMo fraud
  • K Cudjoe
K. Cudjoe. MTN to deploy AI, others to fight MoMo fraud, Oct. 2020. URL https://thebftonline. com/13/10/2020/mtn-to-deploy-ai-others-to-fight-momo-fraud-2/.
32 examples of ai in healthcare that will make you feel better about the future
  • S Daley
S. Daley. 32 examples of ai in healthcare that will make you feel better about the future, 2020. URL
Twitter chooses Ghana as its African headquarters, says its due to country's support of free speech
  • L Daniel
L. Daniel. Twitter chooses Ghana as its African headquarters, says its due to country's support of free speech, Apr. 2021. URL https://www.businessinsider.co.za/ twitter-opensafrica-headquarters-in-ghana-2021-4.
Deep learning in diagnostic healthcare: The future?
  • Enlitic
Enlitic. Deep learning in diagnostic healthcare: The future? 2015. URL https://www.idgconnect. com/article/3579184/deep-learning-in-diagnostic-healthcarethe-future.html.
It's Recruiting Season for AI's Top Talent, and Things Are Get-ting a Little Zany
  • E Winick
E. Winick. It's Recruiting Season for AI's Top Talent, and Things Are Get-ting a Little Zany, 2021. URL https://www.technologyreview.com/2017/12/06/147263/ itsrecruiting-season-for-ais-top-talent-and-things-are-getting-a-little-zany/.
GhIPSS Annual Media Engagement
  • Ghipss
GhIPSS. 2021 GhIPSS Annual Media Engagement, 2021. URL https://ghipss.net/publications. Proceedings of the 27 th SMART-iSTEAMS-IEEE MINTT Conference Academic City University College, Accra, Ghana www.isteams.net/ghana2021
Introducing The New GrainMate GM-102 Grain Moisture Meter
  • Grainmate
GrainMate. Introducing The New GrainMate GM-102 Grain Moisture Meter, 2021. URL https:// sesitechnologies.com/introducing-the-new-grainmate-gm-102-grain-moisturemeter/.
Total data volume worldwide
  • Holst
Holst. Total data volume worldwide 2010-2024, May 2021. URL https://www.statista.com/ statistics/871513/worldwide-data-created/.
Deep Learning Indaba
  • Indaba
Indaba. Deep Learning Indaba, 2021. URL https://deeplearningindaba.com/about/ ourmission/.
History of artificial intelligence in medicine
  • V Kaul
  • S Enslin
  • S Gross
V. Kaul, S. Enslin, and S. Gross. History of artificial intelligence in medicine. Gastrointestinal Endoscopy Journal, 92:807-812, 06 2020. doi: https://doi.org/10.1016/j.gie.2020.06.040. URL https://www.giejournal.org/article/S0016-5107(20)34466-7/pdf.
lpha project: Gov't to install 10,000 cctvs nationwide to fight crime
  • N L Lartey
N. L. Lartey. lpha project: Gov't to install 10,000 cctvs nationwide to fight crime, 2020. URL https://citinewsroom.com/2020/01/ alpha-project-govt-to-install-10000-cctvs-nationwide-to-fight-crime/.
Frontiers in Digital Health
Frontiers in Digital Health, 2020. doi: https://doi.org/10.3389/fdgth.2020.00006.
Understanding AI's Role in WhatsApp Banking -AI and NLP in Ghana and Africa
  • D Owusu
D. Owusu. Understanding AI's Role in WhatsApp Banking -AI and NLP in Ghana and Africa, May 2020. URL https://nokwary.com/blog/2020/05/21/ understanding-ais-role-in-whatsapp-banking/.
PathAI and Gilead Show AI-Powered Pathology Research Models Accurately Interpret Liver Histology in Patients with NASH at AASLD
  • Pathai
PathAI. PathAI and Gilead Show AI-Powered Pathology Research Models Accurately Interpret Liver Histology in Patients with NASH at AASLD 2019, 2019. URL https://www.pathai.com/news/ pathai-gilead-nash-aasld-liver-2019.
High tech for higher ed: An australian engineering professor revamps student learning with teams
  • S Ray
S. Ray. High tech for higher ed: An australian engineering professor revamps student learning with teams.
A 26-year-old is first woman to win the Royal Academy of Engineering's Africa Prize
  • A Salaudeen
A. Salaudeen. A 26-year-old is first woman to win the Royal Academy of Engineering's Africa Prize. URL https://www.cnn.com/2020/09/07/africa/africa-engineering-prize-intl/ index.html.
Developing a functional Natural Language Processing system for the Twi language with limited data. Thesis
  • D Sasu
D. Sasu. Developing a functional Natural Language Processing system for the Twi language with limited data. Thesis, Apr. 2019. URL https://air.ashesi.edu.gh/handle/20.500.11988/507. Accepted: 2020-03-31T13:14:31Z.
The Fourth Industrial Revolution: what it means and how to respond
  • K Schwab
K. Schwab. The Fourth Industrial Revolution: what it means and how to respond, Jan. 2016. URL https://www.weforum.org/agenda/2016/01/ the-fourth-industrial-revolution-what-itmeans-and-how-to-respond/.
Figures of the week: Africa's growing youth population and human capital investments, Sept
  • M Sow
M. Sow. Figures of the week: Africa's growing youth population and human capital investments, Sept. 2018. URL https://www.brookings.edu/blog/africa-in-focus/2018/09/20/ figures-of-the-week-africas-growing-youth-population-and-human-capital-investments/.
African Development Bank provides $1 million for AI-based national customer management systems in Ghana, Rwanda and Zambia
  • O D Terry
O. D. Terry. African Development Bank provides $1 million for AI-based national customer management systems in Ghana, Rwanda and Zambia, Mar. 2021. URL https://www.afdb.org/en/news-and-events/press-releases/ 46. african-development-bank-provides-1-million-ai-based-national-customermanagement-systems-ghana-rwanda-
The future is intelligent: Harnessing the potential of artificial intelligence in africa
  • Y Travaly
  • K Muvunyi
Y. Travaly and K. Muvunyi. The future is intelligent: Harnessing the potential of artificial intelligence in africa, 2020. URL https://www.brookings.edu/blog/africa-in--future-is-intelligent-harnessing-the-potential-of-artificialintelligence-in-africa/.