We use sparse autoencoders (SAEs) to ``brain scan'' large language models, mapping internal activations to interpretable concepts and enabling targeted ``steering'' of specific features. We show that prompting models to adopt specific preferences or demographics causes severe concept contamination, invalidating causal inference in agent simulations. In contrast, SAEs permit smooth, isolated steering of target concepts without collateral activation. Furthermore, our scans reveal a stark disconnect between a model's true internal focus and its self-generated explanations, highlighting flawed introspection. Ultimately, SAEs provide a lightweight intervention for existing models, adding interpretability to embedding-based methods and surgically correcting economic biases.