Ethical AI in Retail: Consumer Privacy and Fairness (Published)
The adoption of artificial intelligence (AI) in retail has significantly transformed the industry, enabling more personalized services and efficient operations. However, the rapid implementation of AI technologies raises ethical concerns, particularly regarding consumer privacy and fairness. This study aims to analyze the ethical challenges of AI applications in retail, explore ways retailers can implement AI technologies ethically while remaining competitive, and provide recommendations on ethical AI practices. A descriptive survey design was used to collect data from 300 respondents across major e-commerce platforms. Data were analyzed using descriptive statistics, including percentages and mean scores. Findings shows a high level of concerns among consumers regarding the amount of personal data collected by AI-driven retail applications, with many expressing a lack of trust in how their data is managed. Also, fairness is another major issue, as a majority believe AI systems do not treat consumers equally, raising concerns about algorithmic bias. It was also found that AI can enhance business competitiveness and efficiency without compromising ethical principles, such as data privacy and fairness. Data privacy and transparency were highlighted as critical areas where retailers need to focus their efforts, indicating a strong demand for stricter data protection protocols and ongoing scrutiny of AI systems. The study concludes that retailers must prioritize transparency, fairness, and data protection when deploying AI systems. The study recommends ensuring transparency in AI processes, conducting regular audits to address biases, incorporating consumer feedback in AI development, and emphasizing consumer data privacy.
Keywords: Data protection, Fairness, algorithmic bias, artificial intelligence (AI), consumer privacy
Development of Secure Cloud-Based Government Solutions (Published)
Government agencies face significant security and efficiency challenges when adopting cloud solutions. These challenges include data breaches, unauthorized access, and compliance with stringent regulatory standards. This paper explores the development of secure and efficient cloud-based solutions tailored specifically for government needs, aiming to address these critical issues. These solutions protect sensitive government data by focusing on robust security protocols, advanced encryption methods, multi-factor authentication, and continuous monitoring. Additionally, integrating technologies such as artificial intelligence and machine learning enhances the ability to predict and mitigate potential threats. Compliance with regulatory standards, such as those set by the National Institute of Standards and Technology (NIST) and ISO 27001, is emphasized to ensure global security adherence. Implementing “Security by Design” and Zero Trust Architecture further strengthens the security framework. This research highlights the importance of a multi-faceted approach, including collaboration with cloud service providers, regular security audits, and employee training programs. Developing secure cloud-based solutions enhances national security and improves public service delivery, making it a vital endeavor for government agencies. Future research should explore emerging technologies and international cooperation to stay ahead of evolving cyber threats.
Keywords: Data protection, National Security, Operational Efficiency, cloud security, government solutions