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The integration of artificial intelligence (AI) into human resource management (HRM) processes has significantly enhanced organisational operations by optimising efficiency in talent acquisition, performance assessment, and employee engagement initiatives. Nonetheless, this integration introduces ethical challenges, including algorithmic bias, data privacy concerns, and the reduction of human decision-making autonomy. The purpose of the review is to systematically examine the ethical considerations associated with AI deployment in HRM, explicitly addressing issues related to bias, transparency, privacy breaches, and job displacement. Additionally, it evaluates strategies for deploying AI responsibly to uphold principles of fairness, accountability, and stakeholder trust. Considering the purpose of the review, a comprehensive systematic review of peer-reviewed literature was conducted, sourcing data from Scopus, Web of Science, and Google Scholar. The review targeted publications relevant to the aims of the study, utilising search keywords such as "ethical consideration," "algorithmic bias," and "data privacy." Data synthesis was conducted via narrative methods complemented by thematic analysis to identify prevailing patterns, research gaps, and emergent themes. Findings revealed that AI enhances HRM functions through automation, predictive analytics, and the delivery of individualised employee experiences, contributing to cost reductions and improved decision-making efficacy. However, potential negative implications include reinforcement of existing biases, violations of privacy, lack of transparency in decision-making processes, and threats to employment stability. Empirical evidence indicates disproportionate impacts on marginalised populations and underscores the necessity for ethical frameworks, including the development and implementation of explainable AI (XAI) and hybrid human-AI systems. Finally, it is affirmed that although AI offers transformative operational benefits within HRM, the associated ethical risks necessitate the implementation of comprehensive governance structures, bias mitigation protocols, and active stakeholder participation. Approaches such as hybrid human-AI systems, targeted workforce training, and adherence to regulatory standards are essential to ensuring sustainable and ethical AI integration. Future research should prioritise investigating employee perceptions and conducting cross-cultural analyses to enhance ethical AI governance in HRM contexts.

Although the growing use of generative artificial intelligence (GAI) in higher education research offers exciting potential for enhancing innovation and efficiency, it is also troubled by significant ethical challenges. This review systematically explores critical concerns that border on risks that are associated with data privacy, algorithmic bias, transparency deficiencies, accountability gaps, and threats to academic integrity. This was done by providing a literature-based assessment of why the use GAI in higher education continues to be problematic. This assessment yielded informative insights that were deciphered from 31 papers which were systematically selected from a sampling universe of 119 publications. Findings highlight the necessity of striking a balance between embracing AI-driven advancements and upholding ethical standards in research, in a manner that does not compromise human capacities to mitigate the unexpected eventualities and their consequences. A set of actionable guidelines that are designed to enhance the responsible integration of GAI in higher education without side-lining the importance of observing critical ethical issues that must be considered and adhered to in academic research is given. These guidelines are useful because they form the foundation of a robust framework which gui, Generative, Higher Education, Researchdes the way GAI is used in higher education research without forgetting that this technology is not an all-round paragon.

This paper critically examined the impact of over-dependence on Artificial Intelligence (AI) by Information Technology (IT) Professionals, exploring the impact on cognitive functions such as critical thinking, decision-making, and problem-solving, and how these affect the autonomy of IT Professionals. Using quantitative research methodology, the study surveyed 180 IT Professionals to gauge their usage of and dependence on AI-powered tools, as well as their perceptions about AI technologies and the effects these might have on their cognitive abilities. The findings showed a very significant integration of AI by the respondents in both their personal and professional capacities, as well as a substantial dependence on AI-powered tools, particularly for decision-making purposes. Furthermore, there appeared to be a possible underestimation of the effects that AI usage has on cognitive abilities, as expressed through the paradoxical survey results. Moreover, several ethical issues were identified such as bias, privacy and the lagging behind of laws and regulations, further serving to complicate the effects of over-dependence on AI. Based on its findings this paper makes recommendations for the development of clear guidelines for the use of AI, continuous learning and development programs, focusing on AI advancements and risks, as well as the implementation of frequent impact and security assessments. These recommendations aim to assist organisations and IT Professionals to benefit from the advantages of AI while maintaining critical skills, human oversight and intuition.

In the context of Industry 5.0, the convergence of Artificial Intelligence (AI), decoloniality, and epistemic justice presents both opportunities and challenges for Human Resource Management (HRM) in the South African public sector. This paper emanates because of the possibility that AI-driven HRM practices could reinforce colonial legacies, repeat systemic bias, and prolong epistemic exclusion if not led by justice-oriented perspectives. This paper aims to explore how concepts of ethical, decolonial, and epistemic justice might inform the use of AI in HRM to promote inclusivity and fairness. This paper employs bibliometric analysis to map and evaluate scholarly discourse on AI, decoloniality, and HRM, aiming to uncover research trends, gaps, and theoretical intersections. Findings indicate that although AI can be efficient and predictive, its application in HRM frequently ignores ethical requirements and local knowledge systems. To protect equity, the study recommends integrating decolonial ideas, participatory methods, and epistemic justice frameworks into HRM technology. This paper concludes that the South African public sector may reconcile technological innovation with transformation and social justice imperatives by ethically aligning AI in Industry 5.0.

Artificial intelligence (AI) is fundamentally transforming accounting practice by displacing traditional data-processing tasks, augmenting auditing functions, and reshaping the professional competency requirements of financial reporting. While these technological developments generate substantial efficiency gains, they simultaneously introduce unprecedented ethical challenges, including algorithmic opacity, accountability diffusion, bias propagation in automated judgment, and the erosion of professional scepticism — that threaten the integrity of financial reporting and the public trust upon which the accounting profession's social licence to operate ultimately depends. This paper develops a rigorous, theoretically grounded analysis of the intersection between artificial intelligence and accounting ethics, drawing on institutional theory, agency theory, virtue ethics, and the stakeholder trust framework to diagnose how AI adoption reconfigures the ethical architecture of financial reporting and to prescribe the governance, professional, and regulatory responses required to safeguard ethical standards and trust in this transformed environment. The paper makes three original contributions: first, it develops a multi-dimensional taxonomy of AI-related ethical risks in accounting, distinguishing technical, professional, and systemic risk categories; second, it proposes the Ethical AI Accounting Governance Framework (EAAGF), an original implementation model that integrates algorithmic governance, professional ethics renewal, regulatory adaptation, and stakeholder assurance mechanisms; and third, it situates these contributions within a comparative empirical analysis of AI adoption experiences across major accounting contexts, integrating evidence from surveys, regulatory reports, and case illustrations. The paper argues that the central challenge is not whether AI will transform accounting — this is already occurring — but whether the profession, regulators, and organisations can collectively construct the ethical governance infrastructure required to ensure that transformation advances rather than undermines the accountability, transparency, and trust that constitute the profession's foundational social purpose.

Artificial intelligence (AI) is progressively revolutionising project management by enhancing forecasting, planning, decision-making, risk evaluation, and organisational efficacy. Nevertheless, escalating apprehensions about trust, accountability, equity, transparency, and human supervision have underscored the necessity for governance strategies that transcend mere technical efficacy. This research performs a concept-focused systematic literature review to investigate the convergence of Ubuntu, African ethics, Human-Centred Artificial Intelligence (HCAI), and project governance. This research consolidates disparate knowledge domains related to responsible AI, HCAI, ethical leadership, stakeholder governance, organisational preparedness, and Ubuntu-informed leadership, stakeholder inclusivity, and a comprehensive Ubuntu-informed human-AI governance framework, utilising peer-reviewed literature from 2022 to 2026. This research posits that Ubuntu offers a unique governance viewpoint rooted in collective responsibility, relational leadership, and human dignity. The results indicate that Ubuntu-informed AI governance can assist African governments and policymakers in establishing ethical and responsible frameworks. This research establishes a basis for subsequent investigations on Ubuntu, artificial intelligence governance, and project management. The results underscore the significance of ethical leadership, stakeholder engagement, human supervision, and responsibility for project management professionals in responsible AI-driven project settings.

Artificial intelligence (AI) is fundamentally transforming accounting practice by displacing traditional data-processing tasks, augmenting auditing functions, and reshaping the professional competency requirements of financial reporting. While these technological developments generate substantial efficiency gains, they simultaneously introduce unprecedented ethical challenges, including algorithmic opacity, accountability diffusion, bias propagation in automated judgment, and the erosion of professional scepticism — that threaten the integrity of financial reporting and the public trust upon which the accounting profession's social licence to operate ultimately depends. This paper develops a rigorous, theoretically grounded analysis of the intersection between artificial intelligence and accounting ethics, drawing on institutional theory, agency theory, virtue ethics, and the stakeholder trust framework to diagnose how AI adoption reconfigures the ethical architecture of financial reporting and to prescribe the governance, professional, and regulatory responses required to safeguard ethical standards and trust in this transformed environment. The paper makes three original contributions: first, it develops a multi-dimensional taxonomy of AI-related ethical risks in accounting, distinguishing technical, professional, and systemic risk categories; second, it proposes the Ethical AI Accounting Governance Framework (EAAGF), an original implementation model that integrates algorithmic governance, professional ethics renewal, regulatory adaptation, and stakeholder assurance mechanisms; and third, it situates these contributions within a comparative empirical analysis of AI adoption experiences across major accounting contexts, integrating evidence from surveys, regulatory reports, and case illustrations. The paper argues that the central challenge is not whether AI will transform accounting — this is already occurring — but whether the profession, regulators, and organisations can collectively construct the ethical governance infrastructure required to ensure that transformation advances rather than undermines the accountability, transparency, and trust that constitute the profession's foundational social purpose.

Artificial intelligence (AI) is revolutionizing the world of research, posing unforeseen opportunities and imminent ethical challenges. This article redefines the ethic of research in AI by advocating for a shift toward a contextual, fair, and accountable approach. It examines critically how the existing standards of ethics are inadequate in addressing the complexity, scale, and lack of transparency of AI systems that are increasingly being used in research settings. By qualitative analysis of recent applications of AI in domains, the paper delineates core ethical issues, including undermining data privacy, algorithmic bias, incomprehensible decision-making, and diminishing accountability. These issues are enhanced in critical domains such as health care, social sciences, and public governance, where ethical errors have the potential to reinforce system injustice or victimize vulnerable populations. The paper argues that ethical responsibility in AI research has to be non-uniform. Rather, it calls for contextual ethics attuned to specific disciplines and cultural milieux in conjunction with justice-based methods that actively work against bias and inequality. Accountability must be designed into research design, not appended, through enforceable policy, continuous ethics education, and multidisciplinary governance. Suggestions include integrating AI-specific ethical norms into institutional policy, fostering open dialogue across disciplines, and developing an ethics culture of foresight. By encouraging a contextual, ethical approach, the research community can ensure that AI does not just accelerate discovery but does so in an equitable, transparent, and socially responsible fashion.

The integration of artificial intelligence (AI) into human resource management (HRM) processes has significantly enhanced organisational operations by optimising efficiency in talent acquisition, performance assessment, and employee engagement initiatives. Nonetheless, this integration introduces ethical challenges, including algorithmic bias, data privacy concerns, and the reduction of human decision-making autonomy. The purpose of the review is to systematically examine the ethical considerations associated with AI deployment in HRM, explicitly addressing issues related to bias, transparency, privacy breaches, and job displacement. Additionally, it evaluates strategies for deploying AI responsibly to uphold principles of fairness, accountability, and stakeholder trust. Considering the purpose of the review, a comprehensive systematic review of peer-reviewed literature was conducted, sourcing data from Scopus, Web of Science, and Google Scholar. The review targeted publications relevant to the aims of the study, utilising search keywords such as "ethical consideration," "algorithmic bias," and "data privacy." Data synthesis was conducted via narrative methods complemented by thematic analysis to identify prevailing patterns, research gaps, and emergent themes. Findings revealed that AI enhances HRM functions through automation, predictive analytics, and the delivery of individualised employee experiences, contributing to cost reductions and improved decision-making efficacy. However, potential negative implications include reinforcement of existing biases, violations of privacy, lack of transparency in decision-making processes, and threats to employment stability. Empirical evidence indicates disproportionate impacts on marginalised populations and underscores the necessity for ethical frameworks, including the development and implementation of explainable AI (XAI) and hybrid human-AI systems. Finally, it is affirmed that although AI offers transformative operational benefits within HRM, the associated ethical risks necessitate the implementation of comprehensive governance structures, bias mitigation protocols, and active stakeholder participation. Approaches such as hybrid human-AI systems, targeted workforce training, and adherence to regulatory standards are essential to ensuring sustainable and ethical AI integration. Future research should prioritise investigating employee perceptions and conducting cross-cultural analyses to enhance ethical AI governance in HRM contexts.

In the context of Industry 5.0, the convergence of Artificial Intelligence (AI), decoloniality, and epistemic justice presents both opportunities and challenges for Human Resource Management (HRM) in the South African public sector. This paper emanates because of the possibility that AI-driven HRM practices could reinforce colonial legacies, repeat systemic bias, and prolong epistemic exclusion if not led by justice-oriented perspectives. This paper aims to explore how concepts of ethical, decolonial, and epistemic justice might inform the use of AI in HRM to promote inclusivity and fairness. This paper employs bibliometric analysis to map and evaluate scholarly discourse on AI, decoloniality, and HRM, aiming to uncover research trends, gaps, and theoretical intersections. Findings indicate that although AI can be efficient and predictive, its application in HRM frequently ignores ethical requirements and local knowledge systems. To protect equity, the study recommends integrating decolonial ideas, participatory methods, and epistemic justice frameworks into HRM technology. This paper concludes that the South African public sector may reconcile technological innovation with transformation and social justice imperatives by ethically aligning AI in Industry 5.0.