A Taxonomy of Systemic Risks from General-Purpose AI
Risto Uuk, Carlos Ignacio Gutierrez, Daniel Guppy, Lode Lauwaert, Atoosa Kasirzadeh, Lucia Velasco, Peter Slattery, Carina Prunkl
TL;DR
The paper develops a foundational taxonomy of systemic risks from general-purpose AI by conducting a systematic review of 86 documents (out of an initial 1,781) to identify 13 risk categories and 50 contributing sources. It adopts the EU AI Act definition of systemic risk to anchor a descriptive, regulatory-relevant framework, and emphasizes transparency and reproducibility with plans for iterative refinement of the taxonomy. The work advances AI safety and governance literature by providing a structured lens to assess large-scale societal harms and informing policymakers on risk prioritization and regulatory development. As a first version, it highlights the interconnected, cumulative nature of risks and sets the stage for ongoing coding, validation, and integration with risk documentation resources like the AI Risk Repository.
Abstract
Through a systematic review of academic literature, we propose a taxonomy of systemic risks associated with artificial intelligence (AI), in particular general-purpose AI. Following the EU AI Act's definition, we consider systemic risks as large-scale threats that can affect entire societies or economies. Starting with an initial pool of 1,781 documents, we analyzed 86 selected papers to identify 13 categories of systemic risks and 50 contributing sources. Our findings reveal a complex landscape of potential threats, ranging from environmental harm and structural discrimination to governance failures and loss of control. Key sources of systemic risk emerge from knowledge gaps, challenges in recognizing harm, and the unpredictable trajectory of AI development. The taxonomy provides a snapshot of current academic literature on systemic risks. This paper contributes to AI safety research by providing a structured groundwork for understanding and addressing the potential large-scale negative societal impacts of general-purpose AI. The taxonomy can inform policymakers in risk prioritization and regulatory development.