Strengthening Artificial Intelligence Governance through Ethical Handling of Sensitive Data: An Applied Study on Text Classification and Differential Privacy

This research develops a comprehensive hybrid framework to enhance Artificial Intelligence governance by ethically managing sensitive textual data through advanced classification techniques. Focusing on natural language processing (NLP) applications, the study integrates rule-based systems, logistic regression, and transformer-based models, notably BERT, to address the challenges of identifying and handling sensitive information within complex and ambiguous linguistic contexts. Experimental results demonstrate that the hybrid model attains an overall classification accuracy of 91%, with precision and recall scores of 89% and 94%, respectively, achieving an F1-score of 92%. These metrics reflect the model’s robustness in real-world scenarios where explicit textual indicators are often lacking. Individually, the rule-based approach excels in precision (98.6%) for clearly identifiable sensitive content, logistic regression ensures perfect recall (100%), capturing all sensitive instances albeit with increased false positives, and the BERT model achieves perfect precision, effectively minimizing false alarms. The hybrid approach synergizes these strengths, resulting in a balanced and reliable classification system. The study further explores the integration of differential privacy via a differentially private logistic regression model using the diffprivlib library, assessing privacy-utility trade-offs at varying privacy budgets (ε = 3, 5, 6). Results reveal that stronger privacy guarantees (lower ε) reduce classification accuracy (78% at ε=3), while looser privacy constraints (ε=6) approach non-private model performance (97% accuracy). These findings underscore the potential of combining hybrid NLP models with differential privacy to deliver scalable, trustworthy, and privacy-preserving AI systems. The proposed framework holds significant relevance for sensitive domains such as healthcare, public administration, and corporate governance, where balancing data privacy and AI performance is critical. Future research should extend these findings by exploring additional privacy configurations and validating the approach against diverse real-world datasets to optimize the equilibrium between privacy protection and analytical effectiveness

Examining Determinants of Real Estate Appraisal Accuracy in Property Business

This study investigates the factors influencing real estate appraisal accuracy, focusing on market dynamics, technological integration, appraiser expertise, and the regulatory framework. The research aims to explore how these factors impact the accuracy of property valuations performed by real estate appraisers in Saudi Arabia. A cross-sectional survey was conducted with 161 licensed real estate appraisers, using a convenience sampling method. Data was collected through a structured questionnaire, and the responses were analyzed using structural equation modeling (SEM). The study found that market dynamics, technological integration, appraiser expertise, and regulatory frameworks significantly influence real estate appraisal accuracy. The findings highlight the importance of these factors in improving the reliability of property valuations, providing valuable insights for real estate professionals, regulators, and policymakers. The findings suggest that real estate appraisers should stay informed about market trends, enhance their technological skills, and continuously develop their expertise to improve appraisal accuracy. Regulatory bodies should strengthen guidelines and standards to ensure consistency in the appraisal process. Policymakers can use these insights to develop strategies that promote trust and stability in the real estate market