With AI transforming daily life and industry alike, global attention is shifting toward the mounting ethical and safety challenges posed by this rapidly evolving technology. From algorithmic bias and opaque decision-making to privacy violations and cybersecurity threats, the challenges posed by AI systems are evolving faster than existing regulatory and ethical frameworks. Given this scenario, a new research paper takes a data-driven approach to understand the state of global scholarship on managing AI risks.
The study, titled “AI Risk Management: A Bibliometric Analysis” and published in Risks (July 2025), analyzes hundreds of academic publications over two decades to identify critical trends, gaps, and future pathways for developing robust and responsible AI risk management systems.
What does the global research landscape on AI risk management look like?
The researchers map the intellectual structure of AI risk management research through bibliometric methods. Drawing from scientific databases like Scopus and Web of Science, the authors analyzed keyword co-occurrences, citation networks, and the frequency of specific thematic clusters to identify how the field has evolved.
The analysis revealed three dominant research axes: sustainable AI, safe and responsible AI, and human-centered AI governance. These themes reflect an increasing scholarly focus on balancing the transformative potential of AI with necessary safeguards to protect users, organizations, and society.
A significant portion of the literature has emerged in the last five years, indicating that interest in AI risk has sharply accelerated alongside advances in machine learning, natural language processing, and autonomous systems. The researchers noted that while early papers primarily discussed ethical or theoretical risks, more recent contributions are engaging with practical implementation, including auditability, algorithmic fairness, and regulatory compliance.
Geographically, the most influential research is concentrated in the United States, China, the United Kingdom, and Germany. These countries have produced leading publications and are at the forefront of both AI innovation and governance debates. However, the authors point out a need for broader global representation, particularly from regions that may face disproportionate impacts from poorly managed AI systems but lack policy influence.
Are current research efforts sufficient to guide practical AI risk management?
Despite the growing volume of literature, the authors argue that current research efforts fall short in one crucial respect: the absence of a comprehensive, quantitative risk management framework for AI. Unlike traditional financial or operational risks, AI risks are often dynamic, complex, and context-dependent. Existing models, the study suggests, do not adequately capture the probabilistic nature of harm, nor do they provide scalable metrics for risk assessment.
The paper emphasizes that many studies offer qualitative insights or propose high-level principles such as fairness, transparency, and accountability. While these are valuable, they are not easily translatable into regulatory practice or enterprise risk planning. The lack of measurable indicators and formal modeling tools creates a gap between theory and implementation.
Another key concern is the siloed nature of existing research. The study highlights that academic work on AI risk is often fragmented across disciplines, including computer science, ethics, economics, and law, with little interdisciplinary integration. This fragmentation hinders the development of unified guidelines and makes it difficult for regulators and practitioners to adopt coherent strategies.
In response to this challenge, the authors advocate for a shift toward empirical, metrics-driven research that can feed into a holistic AI risk taxonomy. This includes incorporating insights from fields like actuarial science, network theory, and behavioral economics to better quantify uncertainty and model cascading effects. Such an approach would support decision-makers in distinguishing between low-probability, high-impact events and more manageable, operational risks.
What are the future priorities for AI risk governance?
For future research, the study identifies several strategic priorities to strengthen the global governance of AI risks. Chief among them is the creation of a quantitative risk management framework tailored specifically for AI systems. This framework would enable organizations to benchmark risk exposure, prioritize mitigation efforts, and align with emerging regulatory standards across jurisdictions.
The authors also recommend fostering international collaboration among academia, industry, and public institutions. Cross-sectoral partnerships can accelerate the development of shared definitions, interoperability standards, and ethical baselines. They note that institutions such as the OECD, ISO, and the EU AI Act are beginning to shape consensus, but more bottom-up research is needed to support their implementation.
Education and transparency are also flagged as essential components. The study calls for capacity-building initiatives that equip AI developers, policy analysts, and compliance officers with tools to understand and manage risk across the AI lifecycle, from data sourcing and model training to deployment and monitoring.
Another key recommendation is the incorporation of “human-in-the-loop” systems wherever AI is used in sensitive or high-stakes domains. These systems ensure that human oversight and ethical reasoning remain part of decision-making, especially when AI models operate in contexts with legal, medical, or financial consequences.
The study also calls for urgent attention to systemic and emergent risks that arise not from individual algorithms but from the interaction of many models across platforms, markets, and infrastructures.
