How does mechanistic interpretability allow researchers to look inside neural network computations?

Historically, deep learning models were treated as inscrutable black boxes where billions of weights produced an output without mechanistic clarity. Through interpretability research, we attach computational probes to isolate specific neural circuits, identifying concepts like deception, situational awareness, or goal-seeking. Understanding the internal representations allows us to verify model alignment with mathematical precision before deployment.