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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 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.