Statistical Rigor for Interpretable AI

We explore **Mechanistic Interpretability (MI)** in AI, focusing on the critical need for **statistical rigor** when analyzing complex neural networks. It explains MI as the process of reverse-engineering AI "black boxes" to understand their **internal computational mechanisms**, a process distinct from traditional interpretability methods. We highlight unique challenges in MI, such as **data abundance but inherent structural complexity**, **polysemanticity** (neurons representing multiple concepts), and the need to identify **monosemantic features** and **causal circuits**. A core argument posits that MI research should adopt stricter **statistical significance thresholds** (e.g., p < .001) due to cheap data generation, while also emphasizing the importance of correctly handling **data dependencies**, interpreting **effect sizes in context**, controlling for **confounding variables**, and utilizing **permutation testing** as a validation "gold standard" for complex analyses. Ultimately, we argue that such **methodological robustness** is crucial for ensuring the reliability and safety of AI systems.