Open Access Digital Management and Governance Review

Seizing AI's Socio-Organizational Vulnerabilities: A Call for Auditable Digital Governance

Dr. Farzand Ali Jan — 2025-10-14

The rapid proliferation of artificial intelligence (AI) technologies has revolutionized digital management and governance, offering unprecedented efficiencies in data processing, decision-making, and resource allocation. From predictive analytics in public administration to automated diagnostics in healthcare, AI promises to enhance organizational performance and societal well-being. However, this advancement is not without peril. Social and organizational vulnerabilities—such as biases embedded in algorithms, privacy breaches, and unequal access to benefits—pose significant risks. These vulnerabilities challenge human intelligence by fostering over-reliance on machines, potentially eroding critical thinking and autonomy. Ethical issues, including fairness, accountability, and the potential for discrimination, further complicate AI's integration. This editorial explores these challenges and proposes strategies for overcoming them, drawing on interdisciplinary insights to advocate for resilient, ethical AI governance in open access digital frameworks. In an era where AI systems process vast datasets to inform decisions in critical sectors like healthcare, finance, and public policy, social vulnerabilities arise from the amplification of existing inequalities. For instance, algorithms trained on biased historical data can perpetuate discrimination against marginalized groups, leading to unequal outcomes in hiring, lending, or law enforcement. The current discourse often highlights ethical concerns in isolation. However, the root problem is structural, stemming from three interlocking areas: AI's inherent vulnerabilities in practice, the insidious challenges to human intelligence, and the pressing need for governance and open accountability.

Citrus Leaves Disease Detection and Classification on Image Recognition Using Deep Convolutional Neural Networks: One Health Perspective

Khalid Mehmood — 2025-10-15

Agriculture production is a crucial economic backbone for any country and is vital in meeting human food needs. At the same time, plant disease poses a significant threat to this sector, leading to decreased yields and heavy losses. Automated systems for disease detection and classification can aid in combating this issue and promoting growth and development. In recent years, deep learning approaches have demonstrated promising results in various artificial intelligence tasks, specifically in the Smart Agriculture domain. Smart Agriculture applications include Plant disease detection, water and soil management, crop distribution, crop cultivation, fruit counting, and yield prediction. This paper presents an integrated and enhanced approach for detecting citrus leaf disease detection using a deep convolutional neural network. The proposed model can distinguish healthy citrus leaves from seven common diseases: bacterial spot, black spot, canker, citrus powdery mildew, greening, melanose, and health. The proposed model extracts the complementary features by incorporating multiple hidden layers and using data augmentation for improved image recognition and classification. The proposed model is tested against other deep learning models on the citrus and Plant Village dataset and outperformed previous studies in various performance measurement metrics. With a test accuracy of 97.66%, our model serves as a reliable tool for citrus plant disease detection.

An Empirical Investigation into the Impact of Technology on Healthcare Outcomes

Muhannad Hamd Abdullah Albaradi — 2025-10-15

Technology has a significant and wide-ranging influence on healthcare outcomes, transforming patient care delivery along the whole healthcare continuum. Technological developments have greatly improved patient experiences and health outcomes, from increasing treatment precision and diagnostic accuracy to increasing patient involvement and enabling people to actively control their own health. Artificial intelligence, telemedicine platforms, digital health solutions, and remote patient monitoring technologies have all been integrated to improve resource allocation, expedite clinical procedures, and enable more accessible and individualized healthcare delivery. The study used a quantitative cross-sectional survey design, and primary data was also collected through a structured questionnaire based on a 5-point Likert scale. The samples were selected through convenient sampling, including the hospital staff as well as the patients. The sample size was determined through a statistical formula after obtaining the results of the pilot study. The pilot size was 271. Based on results, the study concluded that we could optimize the advantages of technology in healthcare delivery by promoting stakeholder engagement, allocating resources for infrastructure, and giving precedence to patient-centered methods. This will ultimately lead to the realization of a healthier and more equal future for all.

Systematic Review: Cryptocurrency and Tax Evasion, Legal Gaps and Regulatory Responses in the Post-Blockchain Era

Abboud Sabriya — 2025-10-15

This systematic review aims to Identify legal gaps enabling cryptocurrency-related tax evasion, evaluate global regulatory responses and their effectiveness in mitigating these gaps, propose a solution model to enhance tax compliance in the cryptocurrency ecosystem, and recommend directions for future research to address persistent challenges. This systematic review applies the PRISMA methodology to synthesize 38 peer-reviewed articles (2020–2025) on cryptocurrency-related tax evasion, focusing on legal gaps and regulatory responses in the post-blockchain era. Cryptocurrencies, enabled by decentralized blockchain technology, challenge tax authorities due to their pseudonymous, cross-border nature. Key findings reveal regulatory fragmentation, enforcement challenges, and emerging frameworks, such as the EU’s Markets in Crypto-Assets (MiCA) regulation and the OECD’s Crypto-Asset Reporting Framework (CARF). A proposed solution model emphasizes international cooperation, blockchain analytics, and standardized tax classifications. The review emphasizes the importance of addressing legal gaps to curb tax evasion while promoting innovation, providing policy recommendations, and outlining future research directions. Future studies should evaluate GCTF’s feasibility, explore privacy-preserving analytics, and investigate global tax treaties. By addressing these issues, policymakers can ensure fiscal accountability while fostering a sustainable cryptocurrency ecosystem. It is expected that if the findings of the study are followed, it will help in reducing tax evasion, enhancing global cooperation, increasing compliance via automation, and balancing innovation.