The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?

The study titled 'The Non-Linear Representation Dilemma: Is Causal Abstraction Enough for Mechanistic Interpretability?' critically examines the popular concept of causal abstraction, which aims to clarify the decision-making processes of machine learning models. Traditionally, interpretability research has relied on the linear representation hypothesis, suggesting that features are encoded linearly in models. However, the authors argue that this linearity is not a requirement for causal abstraction. They provide evidence that any neural network can be mapped to any algorithm under reasonable assumptions, rendering the notion of causal abstraction trivial. This challenges existing frameworks and highlights the need for more robust methods to interpret complex models. The implications of this research extend to the development of machine learning systems, as understanding their decision-making processes is crucial for trust and accountability in AI applications.