Ethical Challenges and Responsible Practices of Artificial Intelligence in Managing Personal Data

The legal and ethical dimensions of artificial intelligence in managing medical records in Oman

This research investigates the legal and ethical implications surrounding the application and management of artificial intelligence (AI) in healthcare record management in Oman, particularly analyzing the role of such aspects for building healthcare professionals’ trust on such AI-based systems. Using international regulations (GDPR, WHO, EU HLEG) and national legislation (Personal Data Protection Law, Royal Decree 6/2022, Executive Regulation 34/2024), the research is quantitatively descriptive. A validated questionnaire was used to collect the data from 309 healthcare professionals in public as well as private healthcare organizations in Oman. The four-factor measurement model consisted of constructs of privacy/data protection, legal framework/accountability, ethical safeguards, and trust in AI. The confirmatory factor analysis confirmed that the four-factor model had a good fit, and the structural equation model analysis explained the determinants of trust. The findings indicate that perceived privacy and data protection were most strongly associated with healthcare professionals’ trust in AI-assisted medical records. Ethical safeguards were also positively associated with trust. By contrast, the perceived legal framework and accountability dimension was negatively associated with trust, suggesting that healthcare professionals may view current legal arrangements as unclear or insufficiently reassuring regarding liability, redress, and accountability. Given the cross-sectional survey design, these results should be interpreted as associations between perceptions rather than causal effects. The study suggests that privacy-by-design practices, clearer accountability procedures, and visible ethical governance may support more trustworthy implementation of AI-enabled medical record systems in Oman.

Developing an Algorithmic Accountability and Data Sovereignty Framework for the governance of Artificial Intelligence platforms in United States education

The paper analyzed regulations, standards, and guidelines on the use of artificial intelligence learning platforms in the United States by conducting a systematic review of documents and legal analysis. The data were gathered by purposely sampling 47 documents, including privacy policies, federal and state legislative documents and reported case studies. The analysis identifies recurring themes, including inadequate consent and transparency mechanisms, legal uncertainty across jurisdictions, and documented risks related to algorithmic decision-making. The document analysis suggests that FERPA and COPPA appear insufficiently adapted to address the data processing capabilities of contemporary Artificial Intelligence platforms, which point to meaningful gaps in student privacy protection. Analysis of reported data shows many schools were using Artificial Intelligence with inadequate district oversight, while 60% of principals and 25% of teachers were currently using AI, although only 18% of districts had developed formal guidelines about its usage. Using Data ethics, technological determinism and surveillance capitalism as analytical lenses, this study showed how current policy architectures may permit the commercial use of student data. Reported Case materials suggest risk of algorithmic bias, discriminatory disciplinary actions, and expanded surveillance practice. The study suggests immediate policy changes that would guarantee control of student data, ensure algorithmic accountability, and also prioritize the benefits of students over data monetization.

Artificial intelligence in medicine: An ethical and legal review.

Artificial Intelligence in HR: Legal and Ethical Aspects of Application and Possibilities of Regulation in Different Labor Markets

The paper’s purpose involves identifying the legal and ethical aspects of artificial intelligence applied in HR and considering the possibilities of regulating this process in different labor markets. This paper defines how AI affects recruitment, performance evaluation, and HR management. This paper considers the need to create international standards and agreements governing AI applications in HR management that would be the legal ground for safeguarding employees’ rights and strike a balance between automation and human factors. The results reveal that AI significantly improves recruitment efficiency and decision-making capabilities in HR by automating candidate screening and analyzing large datasets. The research concludes that successful AI implementation in HR requires balancing technological innovation with ethical and legal considerations. Clear regulations governing the roles and responsibilities of AI developers, operators, and users are essential to prevent discrimination and ensure data privacy. These findings have broader implications for other sectors, such as healthcare and education, that are looking to integrate AI technologies.

Liability Framework to Regulate Artificial Intelligence in Healthcare

Artificial intelligence is beginning to play an important role in contemporary healthcare, where it is applied to diagnosis, clinical support, triage, imaging, remote monitoring, drug development and hospital management. A problem of liability arises because artificial intelligence produces outputs that can contribute to patient risk, yet the system itself cannot bear liability in the way a medical professional, a hospital or a manufacturer does. A review of the literature shows that no dedicated regime governs artificial intelligence liability in healthcare, and it brings out the associated questions of justice, bias, accountability, transparency and information security. This article analyses six areas: patient data confidentiality, patient consent, algorithmic bias and discrimination, misdiagnosis, accountability, and the reuse of previous patient records. It concludes that a workable framework must combine the principles of medical negligence law with data protection, consumer protection and sectoral safety requirements, together with governance mechanisms for artificial intelligence such as human oversight, audit logs, explainability and grievance redressal. The approach is qualitative and comparative, resting on a study of the literature and a content analysis of existing legal instruments, including the Digital Personal Data Protection Act, 2023, the Ayushman Bharat Digital Mission privacy framework, and the guidance of the World Health Organization on artificial intelligence in health. The findings indicate that the existing legal framework offers a partial answer to these questions but not an integrated one. Healthcare artificial intelligence should be treated as a high-risk setting governed by layered liability: the developer answers for design defects and bias, the deploying entity, ordinarily the hospital, answers for implementation and monitoring, and the medical professional answers for negligence where the use of artificial intelligence still involves human judgment.

Ethical Implications of Artificial Intelligence in Human Resource Management

The emergence of artificial intelligence (AI) in human resource management (HRM) has transformed traditional human resource (HR) processes such as recruiting, performance appraisal, employee surveillance, and decision-making. While AI increases efficiency, accuracy, and data-driven decision-making, it also raises ethical challenges for organisations. Ethical Issues of AI in HRM – Algorithmic Bias, Transparency, Data Privacy, Responsibility. Research shows that AI-driven systems can inadvertently reinforce biases in hiring and promotion based on flawed data inputs or constrained design. AI algorithms for HR applications are often inscrutable, leaving HR professionals in a ‘black box’ scenario, where it is difficult to comprehend or justify certain decisions. Like most contemporary topics, ethical issues related to employee data privacy and surveillance can harm employee trust and company culture. Conceptually and analytically, this paper reviews AI ethics and HRM literature together with previously developed frameworks. It highlights the need for ethical standards and responsible deployment of AI to ensure fairness, accountability, and inclusiveness. Results show that AI complements HRM, but ethics must be taken into consideration for responsible and sustainable labour management.