AI Governance: A Systematic Literature Review

Summary

This comprehensive systematic literature review cuts through the noise of AI governance by analyzing 28 carefully selected research papers to map the current landscape of governance solutions. Rather than adding another theoretical framework to the pile, this Springer-published study takes a meta-analytical approach, identifying what actually works (and what doesn't) in existing AI governance frameworks, tools, models, and policies. The researchers structure their analysis around four critical governance questions that get to the heart of practical implementation challenges, making this essential reading for anyone trying to navigate the fragmented world of AI governance research.

The Four Governance Questions That Matter

The study's strength lies in its structured approach to analyzing AI governance through four specific research questions that address real-world implementation gaps:

1. What governance mechanisms currently exist? - A taxonomy of frameworks, policies, and tools already in use
2. How effective are current approaches? - Critical assessment of what's actually working in practice
3. What are the persistent gaps and challenges? - Identification of areas where current solutions fall short
4. Where should future research focus? - Evidence-based recommendations for addressing governance gaps

This framework helps readers move beyond surface-level comparisons to understand the deeper structural issues in AI governance implementation.

Key Research Insights

The systematic review reveals several critical findings that challenge conventional wisdom about AI governance:

Fragmentation Problem: Current governance solutions operate in silos, with limited integration between technical, legal, and organizational approaches. Most frameworks address only narrow aspects of governance rather than providing holistic solutions.

Implementation Gap: There's a significant disconnect between theoretical governance frameworks and practical implementation. Many proposed solutions lack clear guidance on operationalization within existing organizational structures.

Context Sensitivity: One-size-fits-all approaches consistently fail. Effective AI governance requires adaptation to specific sectors, organizational sizes, and regulatory environments.

Measurement Challenges: Most governance solutions lack robust metrics for assessing effectiveness, making it difficult to evaluate success or iterate on approaches.

What Makes This Review Different

Unlike typical literature reviews that simply summarize existing work, this study provides a critical synthesis that:

• Identifies patterns across disparate governance approaches rather than treating each framework in isolation
• Highlights practical implementation challenges that are often overlooked in theoretical papers
• Provides evidence-based recommendations grounded in analysis of what actually works
• Maps relationships between different governance domains (technical, legal, organizational, ethical)

The systematic methodology ensures comprehensive coverage while the analytical framework provides actionable insights for practitioners.

Who This Resource Is For

Researchers and Academics developing new AI governance frameworks or studying governance effectiveness - this provides essential baseline knowledge and identifies research gaps worth pursuing.

Policy Makers at organizational or governmental levels who need to understand the current state of AI governance solutions before developing new policies or selecting existing frameworks.

AI Governance Practitioners implementing governance programs who need evidence-based guidance on what approaches are most likely to succeed in different contexts.

Consultants and Advisors helping organizations navigate AI governance choices - the comparative analysis provides valuable context for recommendations.

Graduate Students in AI ethics, policy, or governance programs who need a comprehensive foundation in current governance research and its limitations.

Limitations to Keep in Mind

As with any systematic review, this study has boundaries that affect its applicability:

• Academic Focus: Primarily analyzes peer-reviewed research, potentially missing innovative governance approaches being developed in industry or government that haven't yet been published
• Rapid Evolution: AI governance is moving quickly; some solutions may have evolved significantly since the papers analyzed were published
• Publication Bias: May overrepresent approaches that have been successful enough to warrant academic publication, potentially missing lessons from failed implementations

The 2024 publication date helps ensure relevance, but the fast-moving nature of AI governance means this should be supplemented with current industry developments.