AI Safety Evaluations Need To Consider Cascading Effects

Current AI safety evaluations are too narrow: Most assessments focus on individual foundation models in isolation rather than examining how different technical and organizational components interact throughout the entire "algorithmic supply chain," potentially missing critical risks that emerge from these interactions.

Cascading effects can amplify problems: The paper demonstrates that when multiple components of an AI system interact—such as model outputs being processed through filters, adapted by different teams, or integrated into larger workflows—problems can compound and produce cumulative downstream consequences that wouldn't be apparent from testing components separately.

Real-world AI systems are socio-technical assemblages: AI safety cannot be evaluated purely as a technical problem; the paper emphasizes that deployed systems involve complex interactions between technical components (like models and filters) and organizational/human elements (like teams and policies), all of which influence actual system behavior and outcomes.

Existing audit approaches have significant gaps: Traditional AI auditing methods fail to capture how different safeguards, adaptations, and modifications work together across the supply chain, leaving blind spots in understanding what could actually go wrong when systems are deployed in practice.

Holistic evaluation frameworks are needed: The paper calls for new research directions that assess AI systems from multiple viewpoints—examining both individual components and broader stakeholder impacts—to identify and prevent cascading failures before systems cause real-world harm.