AI-Powered Platforms for Protecting Women’s Rights and Transforming Widowhood Practices in Southern Nigeria
Article Sidebar
Main Article Content
Abstract
Recently, there is an increasing attention of the media and other organisations on Women Rights violation and several myriad social challenges faced by vulnerable women in the Southern Nigeria. Thus, the study seeks to review these challenges and how the adoption of AI platforms can help to accelerate the protection of women Rights and the practice of widowhood in Southern Nigeria. Findings shows that there must be identification of the fundamental Rights and the women which are vulnerable in this area especially the widows. However, it was established that adoption of AI-platforms to help these women could also have impact on their Rights and protection. Hence, impact assessment is necessary to understand the level of risk associated with the usage of the tool. But most importantly, AI-powered solutions will help in enhancing their lifestyles and sustainable living if adequately deployed and monitored.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Ineli-Ciger, M. (2024). Resettlement by algorithm: Can artificial intelligence uphold human rights?. Computer Law & Security Review, 55, 106051.
Parveen, G., Joshi, P., Uniyal, Y., & Rawat, S. (2024). Contribution of artificial intelligence to improving women’s health in pregnancy. In Artificial Intelligence and Machine Learning for Women’s Health Issues Academic Press.10(1), 25 - 41.
Hussain, S., Ahmad, S., & Wasid, M. (2025). Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study. Computers in Biology and Medicine, 184, 109342.
Yang, Y., Truong, N. D., Maher, C., Nikpour, A., & Kavehei, O. (2021, November). A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2191-2196, IEEE.
Ghaempour, M., Hassanli, K., & Abiri, E. (2024). An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal. Biomedical Physics & Engineering Express, 10(2), 025041.
Li, J., & Carayon, P. (2021). Health Care 4.0: A vision for smart and connected health care. IISE Transactions on Healthcare Systems Engineering, 11(3), 171-180.
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K. & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and translational science, 14(1), 86-93.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature medicine, 25(1), 44-56.
Desai, R., Patel, K., Dave, H., Shah, K., DeWitt, N., Fong, H. K. & Kumar, G. (2020). Nationwide frequency, sequential trends, and impact of co-morbid mental health disorders on hospitalizations, outcomes, and healthcare resource utilization in adult congenital heart disease. The American Journal of Cardiology, 125(8), 1256-1262.
Papp, M., Kőrösi, L., Sándor, J., Nagy, C., Juhász, A., & Ádány, R. (2019). Workforce crisis in primary healthcare worldwide: Hungarian example in a longitudinal follow-up study. BMJ open, 9(7), e024957.
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of ambient intelligence and humanized computing, 14(7), 8459-8486.
Singh, H., Meyer, A. N., & Thomas, E. J. (2014). The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ quality & safety, 23(9), 727-731.
Keller, D. I., Grenier, J., Christé, G., Dubouloz, F., Osswald, S., Brink, M. & Chahine, M. (2009). Characterization of novel KCNH2 mutations in type 2 long QT syndrome manifesting as seizures. Canadian Journal of Cardiology, 25(8), 455-462.
Moss, A. J. & McDonald, J. (1971). Unilateral cervicothoracic sympathetic ganglionectomy for the treatment of long QT interval syndrome. New England Journal of Medicine, 285(16), 903-904.
Benjamin, E. J., Blaha, M. J., Chiuve, S. E., Cushman, M., Das, S. R., Deo, R. & Muntner, P. (2017). Heart disease and stroke statistics—2017 update: a report from the American Heart Association. circulation, 135(10), e146-e603.
Hussain, S., Raza, Z., Giacomini, G. & Goswami, N. (2021). Support vector machine-based classification of vasovagal syncope using head-up tilt test. Biology, 10(10), 1029.
Poddar, M. G., Birajdar, A. C., & Virmani, J. (2019). Automated classification of hypertension and coronary artery disease patients by PNN, KNN, and SVM classifiers using HRV analysis. In Machine learning in bio-signal analysis and diagnostic imaging, Academic Press,99-125
Choi, J., Kim, J. Y., Cho, M. S., Kim, M., Kim, J., Oh, I. Y. & Lee, J. H. (2024). Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms. Heart Rhythm.60 (32) 5088-5760
Carter, S. M., Popic, D., Marinovich, M. L., Carolan, L., & Houssami, N. (2024). Women’s views on using artificial intelligence in breast cancer screening: a review and qualitative study to guide breast screening services. The Breast, 103783.
Bertaina, S., Biganzoli, I., Desiante, R., Fontanella, D., Inverardi, N., Penco, I. G., & Cosentini, A. C. (2025). Fundamental rights and artificial intelligence impact assessment: A new quantitative methodology in the upcoming era of AI Act. Computer Law & Security Review, 56, 106101.
Clarke, R. (2019). Principles and business processes for responsible AI. Computer Law & Security Review, 35(4), 410-422.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.
Gepp, A., Linnenluecke, M. K., O’Neill, T. J., & Smith, T. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, 40(1), 102-115.
Holmström, J. (2022). From AI to digital transformation: The AI readiness framework. Business Horizons, 65(3), 329-339.
Hadzovic, S., Becirspahic, L., & Mrdovic, S. (2024). It's time for artificial intelligence governance. Internet of Things, 27, 101292.
Stahl, B. C., Rodrigues, R., Santiago, N., & Macnish, K. (2022). A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values. Computer Law & Security Review, 45, 105661.
Shaelou, S. L., & Razmetaeva, Y. (2023). Challenges to Fundamental Human Rights in the age of Artificial Intelligence Systems: shaping the digital legal order while upholding Rule of Law principles and European values. In ERA Forum 24(4) 567-587
Ulnicane, I. (2022). Artificial Intelligence in the European Union: Policy, ethics and regulation. In The Routledge handbook of European integrations. Taylor & Francis.10(2), 025041.
Demková, S. (2023). The EU’s Artificial Intelligence Laboratory and Fundamental Rights. Redressing Fundamental Rights Violations by the EU: The Promise of the ‘Complete System of Remedies (Cambridge University Press, 2024).
Smuha, N. A., Ahmed-Rengers, E., Harkens, A., Li, W., MacLaren, J., Piselli, R., & Yeung, K. (2021). How the EU can achieve legally trustworthy AI: a response to the European Commission’s proposal for an Artificial Intelligent 3(5) 66-76
Hernández-Orallo, J., Martínez-Plumed, F., Avin, S., Whittlestone, J., & Ó hÉigeartaigh, S. (2020). AI paradigms and AI safety: mapping artefacts and techniques to safety issues. In ECAI 2020 2521-2528). IOS Press.
Helbing, D. (2019). Machine Intelligence: Blessing or Curse? It Depends on Us!. Towards Digital Enlightenment: Essays on the Dark and Light Sides of the Digital Revolution, 25-39.
Hilb, M. (2020). Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance. Journal of Management and Governance, 24(4), 851-870.
Misra, S. K., Sharma, S. K., Gupta, S., & Das, S. (2023). A framework to overcome challenges to the adoption of artificial intelligence in Indian Government Organizations. Technological Forecasting and Social Change,7(8) 194, -1227
Stahl, B. C., Rodrigues, R., Santiago, N., & Macnish, K. (2022). A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values. Computer Law & Security Review, 45, 105661.
Hleg, A. I. (2019). Ethics guidelines for trustworthy AI. B-1049 Brussels, 6.10(2), 025041.
Hleg, A. I. (2019). Policy and investment recommendations for trustworthy Artificial Intelligence. European Commission-Directorate-General for Communication, Brussels.
Stahl, B. C., Rodrigues, R., Santiago, N., & Macnish, K. (2022). A European Agency for Artificial Intelligence: Protecting fundamental rights and ethical values. Computer Law & Security Review, 45, 105661.
Berendt, B. (2019). AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing. Paladyn, Journal of Behavioral Robotics, 10(1), 44-65.
Coeckelbergh, M. (2021). AI for climate: freedom, justice, and other ethical and political challenges. AI and Ethics, 1(1), 67-72.
Cao, T. M., & Nguyen, L. T. V. (2024). Factors affecting artificial intelligence (AI) adoption in the talent acquisition process: the case of Vietnam’s medium-sized firms. Journal of Asia Business Studies. 55(3) 600-653
Burgess, A., & Burgess, A. (2018). AI in Action. The Executive Guide to Artificial Intelligence: How to identify and implement applications for AI in your organization, 2(4) 73-89.
Chwastek, R. (2017). Cognitive systems in human resources. In 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC, IEEE, 1-4
Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 5088 - 10028.
Sandoval-Almazan, R., Millan-Vargas, A. O., & Garcia-Contreras, R. (2024). Examining public managers' competencies of artificial intelligence implementation in local government: A quantitative study. Government Information Quarterly, 41(4), 101986.
Misra, S. K., Sharma, S. K., Gupta, S., & Das, S. (2023). A framework to overcome challenges to the adoption of artificial intelligence in Indian Government Organizations. Technological Forecasting and Social Change,7(8) 194, -1227
Nwokoro, C. V., & Ogba, F. (2019, January). Widows: Moving from vulnerability to empowerment in Southeast Nigeria. In Women's Studies International Forum (Vol. 72, pp. 56-64). Pergamon.
Abia state Planning Commission. (2010). Abia State Economic Empowerment and Development Strategy (ABSEEDS) report 10(2), 111 – 200
Adegoroye, A. A., & Adegoroye, A. A. (2008). The roles of selected NGOs in economic empowerment of rural women in Ibadanland, Nigeria. Gender and behaviour, 6(2), 1870-1883.
Alese, O. D. (2013). Women and poverty alleviation programmes in Nigeria: The NAPEP approach. Academic journal of interdisciplinary studies, 2(3), 515-521.
Van de Walle, D. (2013). Lasting welfare effects of widowhood in Mali. World Development, 5(1), 1-19.