KRIs for AI Systems

Developing key risk indicators (KRIs) for AI systems is crucial for effective risk management and governance. Based on current best practices, here are some key considerations for developing AI-specific KRIs:

Types of AI Risk Indicators

Technological KRIs
- Model performance metrics (accuracy, precision, recall, F1 score)
- Bias detection rates across protected attributes
- Hallucination rates for generative AI systems
- System errors or malfunctions leading to inaccurate results

Operational KRIs
- Number of AI systems documented and risk-assessed
- Percentage of AI systems with completed risk assessments
- Distribution of AI systems across risk categories (high, medium, low)

Data Quality KRIs
- Percentage of AI systems with complete data lineage tracking
- Number of data transformations documented per AI system
- Metrics for data drift and model drift

Best Practices for Developing AI KRIs

1. Align with organizational objectives: Ensure KRIs are tied to broader business goals and AI governance frameworks.

2. Consider the entire AI lifecycle: Develop indicators for risks at all stages - from data acquisition to model deployment and monitoring.

3. Establish clear thresholds: Define acceptable risk levels and tolerances for each KRI.

4. Implement real-time monitoring: Use automated tools to track KRIs continuously and generate alerts for deviations.

5. Leverage cross-functional expertise: Involve stakeholders from IT, legal, compliance, and business units in KRI development.

6. Regularly review and update: As AI technologies and regulations evolve, periodically reassess and refine your KRIs.