Behavioral Monitoring and Mechanistic Interpretability: A Unified Framework for Scalable AI Oversight

As AI systems become increasingly capable, ensuring safe deployment requires robust methods to verify alignment between model behavior and human intent. Two primary paradigms have emerged: behavioral monitoring, which analyzes outputs and chain-of-thought traces, and mechanistic interpretability, which seeks to understand internal neural computations. Recent position papers from major AI laboratories have framed these as competing approaches. We argue this framing is counterproductive. Through systematic analysis of empirical evidence on chain-of-thought faithfulness and theoretical work on AI control, we demonstrate that these approaches address fundamentally different failure modes and provide complementary safety guarantees. Key findings: (1) behavioral monitoring achieves near-perfect transparency under explicit instruction but fails under implicit conditions; (2) mechanistic interpretability can verify monitoring reliability but faces scaling challenges; (3) neither alone is sufficient, but their principled integration provides more robust oversight. We propose a verification hierarchy, identify concrete research directions, and argue that robust AI safety requires integration of both paradigms rather than treating them as alternatives.