The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability (2408.01416v1)

Abstract: Interpretability provides a toolset for understanding how and why neural networks behave in certain ways. However, there is little unity in the field: most studies employ ad-hoc evaluations and do not share theoretical foundations, making it difficult to measure progress and compare the pros and cons of different techniques. Furthermore, while mechanistic understanding is frequently discussed, the basic causal units underlying these mechanisms are often not explicitly defined. In this paper, we propose a perspective on interpretability research grounded in causal mediation analysis. Specifically, we describe the history and current state of interpretability taxonomized according to the types of causal units (mediators) employed, as well as methods used to search over mediators. We discuss the pros and cons of each mediator, providing insights as to when particular kinds of mediators and search methods are most appropriate depending on the goals of a given study. We argue that this framing yields a more cohesive narrative of the field, as well as actionable insights for future work. Specifically, we recommend a focus on discovering new mediators with better trade-offs between human-interpretability and compute-efficiency, and which can uncover more sophisticated abstractions from neural networks than the primarily linear mediators employed in current work. We also argue for more standardized evaluations that enable principled comparisons across mediator types, such that we can better understand when particular causal units are better suited to particular use cases.

• The paper traces the evolution of neural network interpretability and establishes causal mediation analysis as its core framework.
• It categorizes mediators into types like neurons and attention heads and evaluates them using standard causal metrics.
• It recommends adopting consistent evaluation practices and exploring novel mediator constructs to enhance model diagnostics and fairness.

Overview of "The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability"

The paper "The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability" presents a comprehensive examination of the interpretability of neural networks through the lens of causal mediation analysis. Interpretability is critical for understanding the behaviors of neural networks, yet the field lacks uniformity in theoretical frameworks and evaluative methods. This paper offers a historical context, current insights, and a structured approach to interpretability focused on causal units, or mediators, within neural networks.

History of Interpretability

The paper begins by chronicling the evolution of interpretability within machine learning from the introduction of backpropagation in 1986 to the present era of sophisticated neural architectures. Initial efforts concentrated on manually analyzing simple models, but the rise of larger models, such as AlexNet and Transformers, necessitated scalable interpretability methods. During the 2010s, attention shifted towards visualization and correlation-based methods but with limited causal insights, leading to recent advances in causal interpretability.

Mediator Taxonomy

The core contribution of this work is its framing of interpretability using causal mediation analysis, with mediators as the central construct. Mediators are categorized into types such as neurons, attention heads, and entire layers or submodules, with varying levels of granularity and interpretability. The authors emphasize the need for identifying new mediators that balance computational efficiency with human interpretability, advocating for exploring non-linear and non-neuron-basis-aligned spaces for potentially richer insights into network behaviors.

Current State and Methodologies

The paper surveys existing methods for searching over mediators and highlights the pros and cons associated with various approaches. Standard causal metrics, such as direct and indirect effects, are used to assess mediator influence, but the authors note a lack of standardized evaluations and recommend frameworks for principled comparisons.

Recommendations and Future Directions

To advance the field, the paper calls for:

• Focus on discovering mediators that reveal higher-order abstractions and complex causal interactions within neural networks.
• Developing standard evaluation practices that allow consistent comparison of mediator efficacy across different studies.
• Enhancing the theoretical unity of interpretability research by aligning more closely with causal analysis methodologies.

Implications

The implications of this research are significant both theoretically and practically. Theoretically, adopting a causal framework brings clarity to what aspects of neural computations mediate certain behaviors, allowing for richer explanations of model decisions. Practically, this work lays the groundwork for improved model diagnostics, auditing for fairness and accountability in AI systems, and enhancing generalization through a deeper understanding of model internals.

Conclusion

The paper concludes by speculating on the prospects of AI development facilitated by a rigorous causal understanding of neural networks. With the continued expansion of model capabilities and data-driven tasks, the need for robust interpretability underpinned by causal insights is more pressing than ever. Future developments in AI will likely hinge on the advancements of interpretability techniques such as those discussed in this paper.