This paper highlights the growing concern about whether large-scale neural network models are consistent with human values as they are deployed in high-stakes situations. We propose interpretability, particularly mechanistic approaches, as a solution, arguing that it should be considered a design principle for alignment, rather than a mere diagnostic tool. While post-hoc analysis methods like LIME and SHAP offer intuitive but only correlational explanations, mechanistic techniques like circuit tracing and active patching provide causal insights into internal errors, including deceptive or inconsistent inferences, that behavioral methods like RLHF, adversarial attack testing, and constitutional AI may overlook. However, interpretability faces challenges such as scalability, epistemological uncertainty, and the mismatch between learned representations and human concepts. Therefore, we conclude that progress toward secure and trustworthy AI depends on making interpretability a primary goal of AI research and development, ensuring that systems are not only effective but also auditable, transparent, and aligned with human intent.
