Sep 2026· Journal of Business Practice and Academic Research· pp. 2· 0 citations
Ethics and Social Impacts of AI
TL;DR
The proposed framework treats AI governance as a strategic capacity of an organization, with accountability, transparency, explainability, human oversight and risk management integrated into the AI lifecycle, ensuring responsible innovation, continuous regulatory compliance and sustainable value creation for the organization.
Abstract
With the rapid advancement of Information Technology, including Artificial Intelligence (AI), organisations are innovating much faster, yet at the same time are also dealing with the complexities of regulation and governance. But as AI regulations continue to develop, organizations are faced with a challenge to implement responsible AI and to meet regulations and sustain organizational performance. Although there is previous research on the governance of AI, organizational performance, and regulatory compliance, some have considered them individually and fewer have considered how they can be linked in a cohesive conceptual structure. This research paper aims to fill this gap by presenting an integrated AI Governance Framework that was systematically identified, selected and synthesized from the existing body of scholarly work. Using a conceptual research design approach, the study draws on literature from AI governance, responsible AI, regulatory compliance, and organizational management to create the conceptual pillars of the interrelationships between AI governance, AI compliance, AI innovation, and organizational performance. The proposed framework treats AI governance as a strategic capacity of an organization, with accountability, transparency, explainability, human oversight and risk management integrated into the AI lifecycle, ensuring responsible innovation, continuous regulatory compliance and sustainable value creation for the organization. By bringing together disparate governance perspectives into a coherent conceptual model and offering practical advice for managers and policymakers who are working on designing adaptive AI governance strategies, the framework makes a step toward contributing to AI governance scholarship. It also provides a theoretical basis for future empirical studies to test and improve the suggested framework in various organizational and regulatory settings.
This study examines the theoretical and empirical challenges that AI governance poses
for the fields of organization studies and human resource management (HRM) and proposes
directions for future research. As AI technologies rapidly diffuse across organizations, AI
governance has emerged as a critical managerial con...
Sungjun Kim, Joonghak Lee· Korean Academy of Organizati...· 0 citations
Artificial intelligence (AI) is increasingly used in human resource management (HRM) to streamline processes, enhance decision-making and maintain competitiveness. At the same time, it raises critical concerns around transparency, discrimination, and employee autonomy, which are increasingly discussed in terms of AI go...
Adrian Gegg, Marion Büttgen· German Journal of Human Reso...· 0 citations
This paper explores how AI can be deployed as a pre-review and triage mechanism — supporting global consistency, scalability, and time-to-market — while preserving human judgment, accountability, and regulatory defensibility.
Johanna Anders· Journal of financial complia...· 0 citations
The fast increase in artificial intelligence (AI) in vital points in society has created significant governance issues at the interface of environmental sustainability and legal responsibility. Although there is a rich literature on AI ethics, technical safety, and standalone regulatory compliance, a cohesive theoretic...
Samta Kathuria, Rajesh Singh, Shyamal M. Dave et al.· International Journal for Gl...· 0 citations
Current AI governance strategies and guardrails ensure that AI systems adhere to the ethical principles, legal requirements, and safety standards. However, they create organisational rigidity which could potentially delay new deployments, fail to capture tacit qualitative information, are sometimes unable to enforce th...
M. Nkwo, M. Adamu, Francis Brako et al.· AI and Ethics· 0 citations
This paper explores how AI risk governance can be effectively integrated into the epistemological and structural foundations of such organizations through the lens of fourth-order cybernetics, and offers a conceptual pathway for resilient and ethically aligned AI implementation in complex organizational environments.
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