An approach to systemic risks of AI through the lens of emergence, collective action problems, and externalities
2026-07-20 • Computers and Society
Computers and Society
AI summaryⓘ
The authors explain that new types of widespread risks appear as AI systems become more general and interconnected, but there isn't a clear, agreed-upon definition of these 'systemic risks' in AI. They note that existing ideas often miss how complex interactions can cause big societal harms, like discrimination or environmental damage. To help, the authors suggest a framework that views systemic risks as complex side effects involving feedback loops, market dominance, and dependencies within AI ecosystems. They also highlight problems like information gaps and weak governance that can make these risks worse.
systemic riskgeneral-purpose AIcomplexityemergenceexternalitiescollective action problemsnetwork effectsalgorithmic monocultureAI ecosystemsgovernance
Authors
Carsten Orwat, Lucas Staab, Alexandros Gazos
Abstract
The integration of general-purpose artificial intelligence models into downstream AI systems, among other developments, has given rise to new forms of risk that are more systemic in nature than conventional AI risks. However, there is no generally accepted definition of systemic risks in general and for AI in particular. Conceptualisations of these risks vary across research and regulation. Especially the application of the systemic risk approach to human rights or fundamental rights, like in the EU AI Act, is relatively new, just as the research on the contribution of AI to systemic forms of discrimination, privacy violations, erosions of democracy, or climate and environmental degradation. We argue that some concepts so far have not sufficiently take complexity and emergence into account. Furthermore, this variety of concepts might hinder responsible actors to adequately assess the systemic risks of AI, leading to inadequate prevention and mitigation measures and ineffective governance. To contribute to the understanding of systemic risks of AI, we propose a conceptualisation of systemic risks of AI that considers complex phenomena that lead to the emergence of harms at the societal or global level. We outline systemic risks mainly as complex externalities and collective action problems. Of particular interest are feedback dynamics, processes that lead to market concentration like network effects, algorithmic monocultures, and integration processes of AI supply chains or 'AI ecosystems' and across societal sectors, which can result in structural dominance, (inter-) dependencies, and cascading risks. Further phenomena contributing to systemic risks are information asymmetries, informational emergence, and deficits of the governance and institutional framework.