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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
Explanation Multiplicity: Circuit-Level Interpretability Evidence Does Not Survive Defensible Analytic Variation
TL;DR
This study investigates the reliability of mechanistic interpretability evidence in AI systems, revealing significant variability in claims based on different analytic settings. Results show that 73.2% of claims change under varied conditions, indicating a lack of robustness in circuit-level interpretations.
✦ Why It Matters
Evaluate your AI interpretability methods to ensure they meet regulatory standards and provide consistent evidence.
Key Takeaways
- Assess the robustness of your interpretability claims under different analytic conditions.
- Standardize evaluation metrics to reduce variability in interpretability evidence.
- Document the specific conditions under which interpretability evidence is derived.
- Consider the implications of interpretability instability for compliance with regulations.
How It Works
The study employs a method called circuit discovery, which identifies how specific components of an AI model contribute to its decisions. By mapping these circuits through a deterministic claim map, the researchers can generate structured statements about the model's behavior.
This process involves analyzing various specifications and comparing the results across different analytic settings to assess the stability of the claims.