Isaak Mengesha

Isaak Mengesha

Frontier AI Safety Policies concentrate on prevention: capability evaluations, deployment gates, and usage constraints, while neglecting the capacity to coordinate responses when prevention fails. We argue this coordination gap is structural: investments in ecosystem robustness yield diffuse benefits but concentrated costs, generating systematic underinvestment. Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, we propose that similar mechanisms (precommitment, shared protocols, standing coordination venues) could be adapted to frontier AI governance. Closing the gap requires cross-actor "note-exchange" of ex ante if-then response logic, exposing not only triggers but the decision processes that convert signals into actions. Without such architecture, institutions cannot learn from failures at the pace of relevance.

Isaak Mengesha, Branwen Owen, Charlie Collins et al.

Public AI incident database counts conflate changes in reporting propensity, deployment growth, and shifts in harm frequency per unit of exposure. These issues introduce significant uncertainties challenging public and corporate policy frameworks centred on realized risks. We propose a simple framework that establishes clear points of inquiry, separately estimates exposure from harm-rate trends, and then classifies into meaningful trajectory categories for governance decisions. The framework combines a structured monitoring question format (SORT) to clarify coverage decisions, a tiered estimation procedure calibrated to available evidence, and LLM-assisted incident matching against public databases. Applied to various monitoring questions, we draw conclusions regarding the monitoring ecosystem more broadly: Providing an essential interpretative classification, determining what can and cannot be claimed, and establishing that exposure estimation is required as AI deployments become increasingly common.