Responsible artificial intelligence governance: A review and research framework

Summary

This 2024 scoping review cuts through the noise of AI governance literature to deliver what researchers and practitioners have been waiting for: a unified conceptual framework that makes sense of the fragmented responsible AI governance landscape. Rather than adding another opinion to the pile, the authors systematically analyzed existing research to create a practical framework built on structural and relational dimensions of AI governance. This isn't just academic theory—it's a research-backed roadmap for understanding how responsible AI governance actually works in practice.

What makes this different

Unlike the countless AI ethics guidelines and governance recommendations flooding the field, this resource takes a step back to examine what actually constitutes effective AI governance based on empirical evidence. The authors don't prescribe solutions; instead, they synthesize findings from across disciplines to reveal the underlying patterns and principles that make AI governance work.

The framework's dual focus on structural elements (policies, procedures, organizational design) and relational dimensions (stakeholder engagement, power dynamics, accountability relationships) fills a critical gap in how we think about AI governance beyond just technical controls or policy statements.

Core framework components

• Structural dimensions encompass the formal mechanisms of governance: - Organizational hierarchies and decision-making authorities - Policy frameworks and procedural guidelines - Technical infrastructure and control systems - Legal and regulatory compliance mechanisms
• Relational dimensions capture the human and social aspects:
• Stakeholder engagement and participation processes
• Power distribution and accountability relationships
• Cultural factors and organizational values
• Communication flows and feedback mechanisms

This dual-lens approach acknowledges that effective AI governance isn't just about having the right policies—it's about how those policies interact with organizational culture, stakeholder relationships, and real-world implementation challenges.

Key research insights

The review reveals several critical gaps in current AI governance approaches:

• Most existing frameworks focus heavily on structural elements while underemphasizing relational dynamics
• There's limited empirical evidence on what governance mechanisms actually work in practice
• Cross-sector and cross-jurisdictional coordination remains poorly understood
• The role of non-traditional stakeholders (affected communities, civil society) is often marginalized in governance design

The authors also identify promising research directions, including the need for more longitudinal studies on governance effectiveness and better methods for measuring responsible AI outcomes.

Who this resource is for

• Academic researchers studying AI governance, digital policy, or organizational behavior will find this an essential foundation for understanding the current state of the field and identifying research opportunities.
• Policy professionals at government agencies, international organizations, or think tanks can use the framework to assess gaps in current governance approaches and design more comprehensive strategies.
• Corporate governance teams responsible for AI ethics and risk management will benefit from the structured approach to thinking about both formal controls and organizational culture.
• Graduate students in public policy, technology governance, or related fields will appreciate having a systematic review that synthesizes a fragmented literature into coherent themes.

How to apply this research

Start by mapping your current AI governance approach against both structural and relational dimensions. Most organizations will find they've invested heavily in policies and procedures (structural) while neglecting stakeholder engagement and accountability relationships (relational).

Use the framework to identify specific areas for governance improvement rather than trying to implement everything at once. The research suggests that effective governance requires iterative development and continuous stakeholder feedback.

Consider this framework as a diagnostic tool for evaluating existing governance initiatives or designing new ones. The dual-dimension approach can help ensure you're not missing critical elements that could undermine implementation success.