How do you design effective human oversight for AI systems?
Quick Answer: Combat automation bias with tiered oversight: Essential (all use cases) requires AI literacy, human-cognition-first approach, confidence indicators, and constructive friction. Medium-risk adds domain-specific criteria and pattern monitoring. High-risk requires four-eyes principle, documented reasoning, and adversarial testing.
Key Characteristics:
- Automation bias affects all experience levels—experts show higher bias paradoxically
- Time-pressured professionals seek cognitive shortcuts, increasing bias vulnerability
- COMPAS case study shows oversight failure at scale in criminal justice
- Constructive friction is essential, not optional
Real Example:
The COMPAS criminal justice risk assessment system shows oversight failure at scale, with documented bias against Black defendants despite human review requirements. Judges defaulted to algorithmic recommendations rather than applying independent judgment.
Last Updated: 15 October 2025