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Responsible artificial intelligence governance: A review and research framework

Emmanouil Papagiannidis et al.

Journal of Strategic Information Systems2025https://doi.org/10.1016/j.jsis.2024.101885review
AJG 4ABDC A*
Weight
0.78

Abstract

• Synthesizes empirical studies on responsible AI and the underlying principles. • Differentiates between principles and governance of AI in a responsible way. • Analyzes through a critical lens existing studies and uncovers underlying assumptions. • Defines the notion of responsible AI governance based on the synthesis and critical reflection. • Develops a research agenda and identifies important areas for future research within the IS domain. The widespread and rapid diffusion of artificial intelligence (AI) into all types of organizational activities necessitates the ethical and responsible deployment of these technologies. Various national and international policies, regulations, and guidelines aim to address this issue, and several organizations have developed frameworks detailing the principles of responsible AI. Nevertheless, the understanding of how such principles can be operationalized in designing, executing, monitoring, and evaluating AI applications is limited. The literature is disparate and lacks cohesion, clarity, and, in some cases, depth. Subsequently, this scoping review aims to synthesize and critically reflect on the research on responsible AI. Based on this synthesis, we developed a conceptual framework for responsible AI governance (defined through structural, relational, and procedural practices), its antecedents, and its effects. The framework serves as the foundation for developing an agenda for future research and critically reflects on the notion of responsible AI governance.

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https://doi.org/https://doi.org/10.1016/j.jsis.2024.101885

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@article{emmanouil2025,
  title        = {{Responsible artificial intelligence governance: A review and research framework}},
  author       = {Emmanouil Papagiannidis et al.},
  journal      = {Journal of Strategic Information Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jsis.2024.101885},
}

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Evidence weight

0.78

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact1.00 × 0.4 = 0.40
M · momentum1.00 × 0.15 = 0.15
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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