Prospects for Alignment Automation: Interpretability Case Study
Jacob Pfau, Geoffrey Irving — 2025-03-21 — UK AISI, Google DeepMind — LessWrong
Summary
Proposes a concrete framework for automating interpretability R&D through meta-learning, where AI systems develop interpretability methods by optimizing for improved time-efficiency and accuracy on distributions of downstream behavioral tasks, with detailed protocol, pseudocode, and timeline forecasts (2027-2030).
Source
- Link: https://lesswrong.com/posts/y5cYisQ2QHiSbQbhk/prospects-for-alignment-automation-interpretability-case
- Listed in the Shallow Review of Technical AI Safety 2025 under 1 agenda(s):
- other-interpretability — White-box safety (i.e. Interpretability)