Naming the unseen: How the MIT AI Risk Repository helps map the uncertain terrain of AI governance

From viral social media trends to philosophical debates on socio-environmental impacts, artificial intelligence and its applications are widely recognized as one of the world's most disrupting novelties. While there is broad consensus on the need for AI regulation, the challenge lies in design frameworks that can keep pace with rapid technological advances.

That is why risk-based approaches — like the EU AI Act — have emerged as a promising solution.

Instead of regulating the technology itself, or some of its characteristics, the risk-based regulation is focused on the impacts of AI's application on specific subjects or society as a whole. Yet, foundational questions remain: what constitutes an AI risk? And how can different frameworks be compared or harmonized?

Attempts to organize AI risks are not in short supply. From industry white papers to academic proposals and regulatory definitions, the field is abundant in models that promise to capture what could go wrong when intelligent systems meet the real world.

Yet, paradoxically, this wealth of frameworks can leave professionals more confused than confident. Overlapping terms, inconsistent categories and diverging methodologies make it difficult to compare risks, prioritize mitigation efforts, or align practices with regulatory expectations.

Thus enters the MIT AI Risk Repository. Rather than creating a new model from scratch, the repository synthesizes existing work, acting as a meta-framework that brings order to a fragmented field.