ScienceDirect - Responsible AI Governance Review

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2026·ScienceDirect(peer-reviewed)·sciencedirect.com/science/article/pii/S0963868724000672

Author

Sukanya P

Credibility Rating

4/5

High(4)

High quality. Established institution or organization with editorial oversight and accountability.

Rating inherited from publication venue: ScienceDirect

A peer-reviewed journal article on responsible AI governance frameworks, providing systematic analysis of governance approaches relevant to AI safety policy implementation and regulatory design.

Paper Details

Citations

Year

2026

DOI:10.2139/ssrn.6324138

Metadata

journal articleanalysis

Cited by 1 page

Page	Type	Quality
NIST AI Risk Management Framework (AI RMF)	Policy	60.0

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# Responsible artificial intelligence governance: A review and research framework

Author links open overlay panelEmmanouilPapagiannidisa, PatrickMikalefac, KieranConboyb

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[https://doi.org/10.1016/j.jsis.2024.101885](https://doi.org/10.1016/j.jsis.2024.101885 "Persistent link using digital object identifier") [Get rights and content](https://s100.copyright.com/AppDispatchServlet?publisherName=ELS&contentID=S0963868724000672&orderBeanReset=true)

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## Highlights

- •
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.


## Abstract

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

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Resource ID: 836d3f4c6d8d5432 | Stable ID: YTNhMmU0NT