🤖 AI Summary
This study addresses the absence of a unified framework in current research on systemic risks posed by artificial intelligence, which often overlooks complexity, emergent dynamics, and cross-domain externalities, thereby undermining effective governance. For the first time, it integrates concepts of emergence and collective action dilemmas from complex systems theory into AI risk analysis, synthesizing perspectives from institutional economics, complex systems science, and AI governance to establish a novel paradigm centered on the emergence of societal and global-scale harms. Through conceptual modeling and interdisciplinary theoretical analysis, the work proposes a systemic risk framework encompassing structural dominance, cascading failures, and information asymmetries, offering a robust theoretical foundation for AI policy formulation and risk governance.
📝 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.