Search papers, labs, and topics across Lattice.
This paper challenges the prevailing focus on great power conflict (GPC) in assessing AI-related catastrophic risks by evaluating the significance of non-great-power conflict (NGPC). It tests the null hypothesis that NGPC poses a significantly lower risk than GPC and finds it poorly supported, particularly regarding its potential to escalate great power conflicts and increase catastrophic terrorism. The authors highlight the importance of NGPC in the context of advanced AI, identifying five critical variables that warrant further investigation to mitigate risks effectively.
Non-great-power conflicts could be as critical as great power conflicts in shaping AI risk landscapes, challenging conventional wisdom in the field.
Research on advanced AI and the risk of war has focused almost exclusively on great power conflict, on the grounds that confrontation between nuclear-armed adversaries poses the greatest risk of catastrophic or existential harm. Considerably less attention has been paid to non-great-power conflict (NGPC): wars between non-great powers, between non-great powers and great powers, civil wars, proxy wars, and conflicts involving nonstate actors. This paper evaluates the null hypothesis that NGPC is much less important than great power conflict (GPC) as a source of catastrophic risk in an era of increasingly capable AI, against the alternative that it is within an order of magnitude of GPC in importance. We assess three sub-hypotheses: that NGPC increases the likelihood of great power conflict; that it increases the expected harm from catastrophic terrorism; and that it increases the expected harm from loss of control over advanced AI systems. We find the null poorly supported for H1 and H2, and identify H3 as a priority for further work rather than a settled finding. We also identify five intermediate variables that recur across the pathways - information environment quality, decision-making timeline compression, great power threat perception, capability diffusion, and norm erosion - and argue that these shared nodes are the highest-priority targets for further investigation and intervention.
