The application of artificial intelligence (AI) technologies in the public sector has led to improved public services, enhanced administrative performance, and strengthened automated decision-making. However, the increasing reliance on AI systems has raised concerns regarding accountability, ethical compliance, privacy protection, transparency, human oversight, and risk management. This study, employing both conceptual and qualitative research methodologies, examines the governance factors and requirements for responsible AI implementation in the public sector. The research methodology includes a comparative analysis of international AI governance frameworks and regulations. The study identifies key dimensions influencing responsible AI implementation, such as accountability, human oversight, ethical governance, legal compliance, risk management, and transparency. The findings demonstrate that the adoption of responsible AI cannot be achieved through technological means alone but also requires a commitment to comprehensive governance mechanisms. Furthermore, the sequential interaction and interdependence of governance factors reduce operational and societal risks, increase transparency and explainability, and foster public trust in the systems. This study contributes to enriching the culture and knowledge of AI governance, and the proposed framework helps government sector leaders develop responsible AI governance in accordance with international standards and regulations.
Ghazwan Hani Hussein, Faiza Mohamed, A. Abuzreda· Journal of Technology and Sy...· 0 citations
Purpose: This study aims to evaluate the effectiveness of machine learning, deep learning, and ensemble learning methods for the early diagnosis of oral cancer using multi-risk epidemiological and behavioural data collected from several countries. The main goal is to examine whether non-diagnostic factors can support early prediction before clear clinical signs appear.
Design/Methodology/Approach: The study used a dataset containing 84,922 records, including pre-diagnosis and post-diagnosis variables. Post-diagnosis variables were used only for interpretation and analysis to avoid data leakage. Several models were tested, including Random Forest, Logistic Regression, Support Vector Machine, XGBoost, LightGBM, TabNet, MLP classifier, and a voting-based ensemble model. The models were evaluated using accuracy, recall, precision, F1-score, and ROC-AUC.
Research Limitation: The main limitation is that the study did not use clinical diagnostic features, medical imaging, genetic markers, or laboratory biomarkers, which may improve prediction performance.
Findings: The results showed that when only non-diagnostic epidemiological and behavioural features were used, all models achieved performance close to random classification. This means that early prediction of oral cancer using these features alone is difficult. The study also showed clear regional and economic differences among countries in oral cancer prevalence, feature importance, treatment costs, and productivity losses.
Practical Implication: The findings suggest that epidemiological data should be combined with clinical and biomarker-based data to develop more accurate early diagnosis systems.
Social Implication: The study highlights the need for better awareness, early screening programs, and improved access to diagnostic services, especially in developing countries.
Originality/Value: This study provides a realistic evaluation of oral cancer early prediction using non-diagnostic data and emphasises the importance of avoiding data leakage in medical AI studies.
G. H. Hussein, S. Elbai, K. Elayati et al.· African Journal Of Applied R...· 1 citation
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