Parasite Features: Causal Abstraction Through Spurious Pathways

Causal Abstraction can be used to define a notion of equivalence between a neural network and an algorithm, thus serving as a powerful framework for mechanistic interpretability. In this paper, however, we show the existence of a class of spurious features which straightforwardly pass such equivalence tests. Suppose there are two directions in a model's representation space where: (i) one discriminates the input based on some specific feature, and (ii) the other steers the model towards a certain output. Causal Abstraction techniques can create a pathway leveraging these unrelated directions to simulate their combined behavior in the model, which thus spuriously appears to implement a certain feature. In such cases, we say the method discovered a parasite feature. Experimentally, we show that Distributed Alignment Search—a Causal Abstraction-based method—finds parasite features using linear alignment maps in two tasks: syntactic agreement and two-digit addition. Our paper highlights an important consideration for Causal Abstraction and contributes to the ongoing debate about best practices for mechanistic interpretability.