Second-Order Risks
Second-order risks result from the consequences of first-order risks and relate to the risks resulting from an ML system interacting with the real world, such as risks to human rights, the organization, and the natural environment.
ENTITY
3 - Other
INTENT
3 - Other
TIMING
2 - Post-deployment
Risk ID
mit197
Domain lineage
6. Socioeconomic and Environmental
6.0 > Socioeconomic & Environmental
Mitigation strategy
1. Implement continuous, real-time AI Observability and performance monitoring systems post-deployment to track for unexpected system drift, emergent second-order risks, and deviations from intended outcomes that could impact human rights, the organization, or the natural environment. 2. Mandate enhanced model interpretability and explainability (e.g., via LIME or Shapley values) for all high-stakes applications to enable objective assessment of algorithmic fairness, auditability of decisions, and compliance with anti-discrimination and transparency mandates. 3. Establish a formal enterprise-wide AI governance and accountability framework that clearly defines human responsibility for AI/ML outcomes and includes an established risk acceptance strategy, ensuring accepted second-order risks remain within acceptable organizational and ethical parameters.