New
(2025) AI Processed Human Reviewed
Responsible artificial intelligence governance:A review and research framework
This scoping review synthesizes existing research on responsible artificial intelligence (AI) principles and governance practices across the complete AI project lifecycle. The study develops a holistic conceptual framework that details structural, procedural, and relational practices along with their antecedents and organizational and societal effects.
Problem
Despite widespread guidelines and high-level principles for ethical AI, organizations struggle to translate abstract concepts into actionable practices. Literature in this domain remains disparate and lacks clarity on how to operationalize responsible AI governance while balancing business competitiveness, ethical risks, and compliance requirements.
Outcome
- Synthesizes prior research on responsible AI into seven core principles: accountability, diversity and fairness, human agency and oversight, privacy and data governance, technical robustness and safety, transparency, and social and environmental well-being.
- Establishes a tripartite framework for responsible AI governance comprising structural (roles and authority), procedural (processes and evaluation), and relational (collaboration and literacy) practices.
- Maps the contextual antecedents of AI governance, including evolving societal norms and organizational values, as well as its effects on business value and social assessment.
- Formulates a detailed research agenda with key questions to guide future academic inquiry and practical implementation.
- Establishes a tripartite framework for responsible AI governance comprising structural (roles and authority), procedural (processes and evaluation), and relational (collaboration and literacy) practices.
- Maps the contextual antecedents of AI governance, including evolving societal norms and organizational values, as well as its effects on business value and social assessment.
- Formulates a detailed research agenda with key questions to guide future academic inquiry and practical implementation.
What it means for you
- CIO / IT Executive: On Monday morning, initiate a review of your organization's current AI project lifecycle documentation to identify where the seven core responsible AI principles (accountability, diversity & fairness, human agency & oversight, privacy & data governance, technical robustness & safety, transparency, and social & environmental well-being) are explicitly addressed and where gaps exist, preparing to assign ownership for these areas.
- IT Manager: On Monday morning, schedule a 30-minute meeting with your team responsible for AI development or deployment to collaboratively identify one specific AI project that could benefit from enhanced procedural practices (e.g., implementing a new risk assessment checklist based on transparency principles) and assign a team member to draft this enhancement by Friday.
- Business Strategist: On Monday morning, dedicate an hour to researching how responsible AI governance (structural, procedural, and relational practices) could directly contribute to your organization's stated business objectives and competitive advantage, identifying at least one concrete opportunity to explore further.
- Researcher: On Monday morning, select one of the seven core responsible AI principles that you find most underdeveloped in current practice and spend two hours identifying three specific research questions from the provided framework that directly address operationalizing this principle, preparing to draft a short proposal for investigation.
- Policymaker: On Monday morning, dedicate an hour to identifying which of the seven core responsible AI principles your current regulatory framework or internal policies address most weakly, and then draft a one-page summary of potential areas for future policy development or amendment, focusing on bridging the gap between abstract principles and actionable practices.