  Abstraction -- the process of generalizing specific examples into broad
reusable patterns -- is central to how people efficiently process and store
information and apply their knowledge to new data. Promisingly, research has
shown that ML models learn representations that span levels of abstraction,
from specific concepts like "bolo tie" and "car tire" to more general concepts
like "CEO" and "model". However, existing techniques analyze these
representations in isolation, treating learned concepts as independent
artifacts rather than an interconnected web of abstraction. As a result,
although we can identify the concepts a model uses to produce its output, it is
difficult to assess if it has learned a human-aligned abstraction of the
concepts that will generalize to new data. To address this gap, we introduce
abstraction alignment, a methodology to measure the agreement between a model's
learned abstraction and the expected human abstraction. We quantify abstraction
alignment by comparing model outputs against a human abstraction graph, such as
linguistic relationships or medical disease hierarchies. In evaluation tasks
interpreting image models, benchmarking language models, and analyzing medical
datasets, abstraction alignment provides a deeper understanding of model
behavior and dataset content, differentiating errors based on their agreement
with human knowledge, expanding the verbosity of current model quality metrics,
and revealing ways to improve existing human abstractions.
