Evaluation Metric Limitations

Standardized evaluation metrics capture only narrow slices of AI system performance. High benchmark scores don’t guarantee real-world utility; low scores don’t mean a system is useless.

The Goodhart Problem

When a measure becomes a target, it ceases to be a good measure. Systems optimized for benchmark performance may game metrics in ways that don’t transfer to actual use.

Common Limitations

Narrow Coverage: Benchmarks test specific capabilities, not holistic performance Distribution Mismatch: Benchmark data differs from production data Static Evaluation: One-time tests miss drift and degradation Human Ceiling Assumptions: Benchmarks assume human performance is the gold standard

What Metrics Miss

• User satisfaction and trust
• Graceful degradation under pressure
• Edge case handling
• Long-term reliability
• Contextual appropriateness

Implication

Benchmark performance is necessary but not sufficient. Complement standardized metrics with task-specific evaluation, user testing, and production monitoring.

Related: 05-atom—evaluation-metric-limitations, 03-atom—benchmark-ecological-validity