arXiv (Cornell University) · 2023 · 58 citations · 0 references
ComputingComputer ScienceSystems EngineeringArtificial IntelligenceComputer SecurityRisk AssessmentCyber RiskModel Risk ManagementRisk EngineeringThreat AnalysisThreat AssessmentThreat ModelingAi Capability DevelopmentAi Risk AssessmentAi Risk ManagementArtificial Intelligence RiskCapability AssessmentModel Performance EvaluationSecurity Of Ai SystemsTraining
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities. Further progress in AI development could lead to capabilities that pose extreme risks, such as offensive cyber capabilities or strong manipulation skills. We explain why model evaluation is critical for addressing extreme risks. Developers must be able to identify dangerous capabilities (through "dangerous capability evaluations") and the propensity of models to apply their capabilities for harm (through "alignment evaluations"). These evaluations will become critical for keeping policymakers and other stakeholders informed, and for making responsible decisions about model training, deployment, and security.