When evaluating high-impact interventions, such as those in AI policy or global health, overlooking potential risks can lead to catastrophic outcomes. Current robustness assessment methods in the Effective Altruism (EA) community may suffer from unconscious biases, where researchers unintentionally focus on familiar risks while missing others. A more systematic approach could help identify and mitigate these blind spots, leading to better-informed decisions.

A Three-Step Approach to Robustness Assessment

One way to address this gap is through a structured process that separates problem identification from evaluation. First, a simplified "toy model" of the world relevant to the intervention could be created, capturing key variables and relationships. Next, hundreds of simulations could be run to explore how the intervention might perform under different conditions, generating a wide range of outcomes—both positive and negative. Finally, only after simulations are complete, the likelihood and preventability of bad outcomes could be assessed. This delayed evaluation helps reduce biases, such as anchoring or confirmation bias, that might otherwise skew which risks are considered.

Potential Applications and Stakeholders

This method could be particularly useful for EA researchers, who need rigorous tools to evaluate interventions, and policymakers in high-stakes fields like AI, who rely on robust evidence. Organizations funding EA projects might also benefit, as better assessments could improve resource allocation. An initial version could start as a simple checklist or framework, tested on smaller projects before scaling to automated software or platforms. Pilot testing on high-profile interventions, such as AI governance proposals, could demonstrate its value and encourage broader adoption.

Challenges and Refinements

Key challenges include ensuring model accuracy and capturing rare but critical risks. Techniques like validating models against historical data, importance sampling (to emphasize low-probability scenarios), and adversarial simulation (testing extreme conditions) could help. Resistance to new methods might be addressed by showcasing successful case studies. If adopted widely, this approach could provide a more objective and comprehensive way to assess the robustness of high-impact interventions.