The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.

This research analyses the evolving intersection of generative AI and healthcare. It explores the transformative potential of integrating generative AI in healthcare, particularly in process automation, patient care, patient monitoring, and diagnosis. However, the implementation of generative AI in healthcare faces challenges like medical privacy concerns, the possibility of errors and injuries, and regulatory compliance challenges. The research proposes solutions to the challenges of implementing generative AI to enhance patients’ health outcomes. The study demonstrates the importance of medical privacy and transparency in addressing privacy issues. Dealing with errors and injuries requires AI systems to be trained on complete data and quality oversight. Addressing regulatory compliance problems requires providers to be more proactive in engaging authorities and helping them understand the importance of creating AI-based laws in healthcare. The solutions allow researchers, providers, policymakers, and other stakeholders to fast-track the implementation of generative AI in healthcare while mitigating possible negative implications.

The rapid expansion of artificial intelligence (AI) and digital health technologies has made data sovereignty a critical policy concern. In Saudi Arabia, Vision 2030 emphasizes technological innovation, economic diversification, and resilient digital infrastructures, positioning data governance as a foundation for national transformation. This review examines how data sovereignty strategies can support the Kingdom’s digital health transformation while ensuring compliance with international standards. Using a qualitative approach, the study synthesizes policy documents, institutional reports, and peer-reviewed literature. The analysis identifies both enablers and barriers: strong institutions such as the Saudi Data and Artificial Intelligence Authority (SDAIA) and the National Data Management Office (NDMO), alignment of the Personal Data Protection Law (PDPL) with frameworks like the GDPR, but also challenges including rigid intellectual property rules, fragmented cloud data governance, and weak sector-specific policies. The study’s objectives are to clarify these challenges, assess their impact on generative AI adoption, and recommend reforms that align with Vision 2030’s innovation goals. Policy recommendations include adaptive IP frameworks, sector-specific governance models, inter-agency coordination, and public–private partnerships to expand access to lawful datasets. The findings demonstrate that data sovereignty, when strategically designed, functions not as a barrier but as an enabler of secure, scalable, and ethically responsible innovation in healthcare and beyond. This work contributes actionable insights for policymakers, regulators, and researchers in Saudi Arabia and offers lessons for other nations navigating the balance between sovereignty and competitiveness in the digital era.

As Saudi Arabia advanced its Vision 2030 agenda, Generative Artificial Intelligence (GenAI) emerged as a transformative force in software engineering. Using socio-technical systems (STS) theory, which framed GenAI adoption as an interplay between technological capability and organizational social structure, this study examined how tools such as GitHub Copilot, Baseer, and ChatGPT were reshaping software development workflows, engineering roles, and governance practices across the Kingdom’s public and private sectors. Drawing on case studies from SDAIA, STC, and KAUST, the research analyzed real-world GenAI integration alongside national initiatives such as SDAIA’s Arabic Large Language Model (ALLaM) and the Personal Data Protection Law (PDPL), which anchored responsible AI deployment. The analysis identified bias, explainability gaps, and over-reliance as the principal risks accompanying adoption. The findings suggested that GenAI adoption shortened software development cycles, improved code localization for the Saudi context, and supported compliance with emerging governance requirements. Based on these findings, the study proposed a strategic roadmap centered on ethical alignment, localized innovation, and inclusive workforce development. This work contributed to both academic scholarship and national AI policy by connecting GenAI deployment with Saudi Arabia’s cultural values, governance standards, and long-term digital transformation goals.

The rapid expansion of the Internet of Things (IoT) has introduced an unprecedented number of interconnected devices, creating new opportunities for automation and data exchange but simultaneously increasing the attack surface for cyber threats. Among these, botnet attacks have emerged as one of the most severe threats, capable of compromising massive networks of IoT devices for malicious purposes such as Distributed Denial of Service (DDoS) and data exfiltration. This research proposes a Lightweight Deep Learning-based Intrusion Detection System (DL-IDS) designed to efficiently detect IoT botnet activities with high accuracy and minimal computational overhead. The proposed model integrates a hybrid Autoencoder–Long Short-Term Memory (LSTM) architecture that captures both spatial and temporal traffic features. The system preprocesses raw network data through feature selection, Min–Max normalization, and class balancing using SMOTE, before training on benchmark datasets such as BoT-IoT and UNSW-NB15. Evaluation results demonstrate that the model achieves an overall accuracy of 99.4%, precision of 99.2%, and AUC score of 0.997, outperforming traditional machine learning and hybrid deep learning baselines (CNN–LSTM, GRU-hybrid, and SVM-Ensemble). Furthermore, the lightweight design of the model ensures its deployability in real-time edge computing environments. Experimental validation on a Raspberry Pi 4 (8 GB) confirms that the proposed DL-IDS maintains low latency and minimal memory consumption, making it well-suited for IoT gateway and edge-level applications. This study concludes that lightweight deep learning frameworks can effectively balance detection accuracy and computational efficiency, contributing significantly to securing modern IoT infrastructures against evolving botnet threats.

Artificial intelligence (AI) offers significant opportunities to advance sustainable water resource management in Saudi Arabia, a country challenged by arid conditions, rapid population growth, and high agricultural demand. By integrating machine learning with real-time environmental sensing, AI enables continuous monitoring and accurate forecasting of water quality and availability, facilitating proactive interventions to prevent pollution and optimize usage. Precision irrigation systems powered by AI adjust water delivery based on soil moisture, weather forecasts, and crop needs, reducing waste while supporting food security. Coupled with Internet of Things (IoT) devices and remote sensing technologies, these systems provide enhanced spatial and temporal data coverage for comprehensive analysis and adaptive management. Key challenges include ensuring data quality, addressing ethical and regulatory concerns, and strengthening technical capacity among users. Collaborative governance frameworks and public-private partnerships are essential to align technological solutions with societal values and sustain engagement. Embedding AI-driven approaches within national strategies such as Vision 2030 supports environmental preservation, economic resilience, and compliance with international sustainability commitments. This integrated approach enhances water conservation, improves resource allocation efficiency, and strengthens climate resilience in the face of complex hydrological and socio-economic pressures.