Explore the evidence.
Understand the reach.

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 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.

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.