Interpretability and Transparency

Interpretability aims to understand and explain what AI models do internally. This sub-article expands Section 4.2 of Philosophy.

Why Interpretability Matters

• Trust, safety, debugging, and accountability.
• Without it, we cannot reliably predict failures or correct misalignment.

Approaches

• Mechanistic interpretability: reverse-engineering internal representations (e.g., sparse autoencoders, feature circuits).
• Post-hoc explanations: saliency maps, attention patterns, and natural-language justifications.
• Transparency: publishing system cards, data cards, and evaluation results.

Limitations

• Explanations may be unfaithful to the actual computation.
• Large models resist simple global explanations.
• Trade-offs with performance and privacy.

Why It Matters

Interpretability is a practical tool for alignment and governance, not just a philosophical goal.

Sources

• Olah, C. et al. (2020). Interpretability via feature extraction.
• Anthropic. Sparse autoencoders research.

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