AI Safety Papers

Mechanistic Interpretability for AI Safety - A Review

Authors

Leonard Bereska, E. Gavves

Abstract

Understanding AI systems' inner workings is critical for ensuring value alignment and safety. This review explores mechanistic interpretability: reverse engineering the computational mechanisms and representations learned by neural networks into human-understandable algorithms and concepts to provide a granular, causal understanding. We establish foundational concepts such as features encoding knowledge within neural activations and hypotheses about their representation and computation. We survey methodologies for causally dissecting model behaviors and assess the relevance of mechanistic interpretability to AI safety. We examine benefits in understanding, control, alignment, and risks such as capability gains and dual-use concerns. We investigate challenges surrounding scalability, automation, and comprehensive interpretation. We advocate for clarifying concepts, setting standards, and scaling techniques to handle complex models and behaviors and expand to domains such as vision and reinforcement learning. Mechanistic interpretability could help prevent catastrophic outcomes as AI systems become more powerful and inscrutable.

Publication Details

  • Published: Unknown
  • Added to AI Safety Papers: 2024-11-11
  • Original Paper: Link

Summary

This comprehensive review paper examines mechanistic interpretability, an emerging approach to understanding the inner workings of AI systems that aims to reverse engineer neural networks into human-understandable algorithms and concepts.

Figure 1: Interpretability paradigms offer distinct lenses for understanding neural networks: Behavioral analyzes input-output relations; Attributional quantifies individual input feature influences; Concept-based identifies high-level representations governing behavior; Mechanistic uncovers precise causal mechanisms from inputs to outputs.
Figure 1: Interpretability paradigms offer distinct lenses for understanding neural networks: Behavioral analyzes input-output relations; Attributional quantifies individual input feature influences; Concept-based identifies high-level representations governing behavior; Mechanistic uncovers precise causal mechanisms from inputs to outputs.

Figure 1 The paper begins by contrasting mechanistic interpretability with other interpretability paradigms: - Behavioral: Analyzes input-output relationships - Attributional: Quantifies individual input feature influences - Concept-based: Identifies high-level representations - Mechanistic: Uncovers precise causal mechanisms from inputs to outputs The authors establish several core concepts and hypotheses:

Figure 2: Comparison of privileged and non-privileged basis in neural networks. Figure adapted from (Bricken et al., 2023).
Figure 2: Comparison of privileged and non-privileged basis in neural networks. Figure adapted from (Bricken et al., 2023).

Figure 2

  1. Features as fundamental units of representation

  2. Linear representation hypothesis - features are directions in activation space

  3. Superposition hypothesis - networks represent more features than neurons through overlapping combinations

  4. Universality hypothesis - similar circuits emerge across models trained on similar tasks The paper outlines key methodological approaches:

[See Figure 7 in the original paper]

  1. Observational methods: - Structured probes - Logit lens variants - Sparse autoencoders

  2. Interventional methods: - Activation patching - Attribution patching - Causal scrubbing The authors discuss the relevance to AI safety:

[See Figure 11 in the original paper]

Benefits: - Accelerating safety research - Anticipating emergent capabilities - Monitoring and evaluation - Substantiating threat models Risks: - Accelerating capabilities - Dual-use concerns - Diverting resources - Causing overconfidence The paper concludes by identifying key challenges and future directions:

  1. Need for comprehensive, multi-pronged approaches

  2. Scalability challenges

  3. Technical limitations in bottom-up interpretability

  4. Adversarial pressure against interpretability Figure 1 makes the best thumbnail as it clearly illustrates the key distinctions between different interpretability approaches and provides an accessible entry point to understanding the paper’s framework. The review makes several important contributions:

  5. Provides the first comprehensive synthesis of mechanistic interpretability research

  6. Establishes clear terminology and conceptual framework

  7. Identifies key challenges and future research directions

  8. Analyzes implications for AI safety This paper serves as both an accessible introduction for newcomers and a valuable reference for researchers in the field, while highlighting the critical importance of interpretability for ensuring safe and aligned AI systems.