  Mechanistic interpretability work attempts to reverse engineer the learned
algorithms present inside neural networks. One focus of this work has been to
discover 'circuits' -- subgraphs of the full model that explain behaviour on
specific tasks. But how do we measure the performance of such circuits? Prior
work has attempted to measure circuit 'faithfulness' -- the degree to which the
circuit replicates the performance of the full model. In this work, we survey
many considerations for designing experiments that measure circuit faithfulness
by ablating portions of the model's computation. Concerningly, we find existing
methods are highly sensitive to seemingly insignificant changes in the ablation
methodology. We conclude that existing circuit faithfulness scores reflect both
the methodological choices of researchers as well as the actual components of
the circuit - the task a circuit is required to perform depends on the ablation
used to test it. The ultimate goal of mechanistic interpretability work is to
understand neural networks, so we emphasize the need for more clarity in the
precise claims being made about circuits. We open source a library at
https://github.com/UFO-101/auto-circuit that includes highly efficient
implementations of a wide range of ablation methodologies and circuit discovery
algorithms.
