  Machine learning models are often brittle on production data despite
achieving high accuracy on benchmark datasets. Benchmark datasets have
traditionally served dual purposes: first, benchmarks offer a standard on which
machine learning researchers can compare different methods, and second,
benchmarks provide a model, albeit imperfect, of the real world. The
incompleteness of test benchmarks (and the data upon which models are trained)
hinder robustness in machine learning, enable shortcut learning, and leave
models systematically prone to err on out-of-distribution and adversarially
perturbed data. The mismatch between a single static benchmark dataset and a
production dataset has traditionally been described as a dataset shift. In an
effort to clarify how to address the mismatch between test benchmarks and
production data, we introduce context shift to describe semantically meaningful
changes in the underlying data generation process. Moreover, we identify three
methods for addressing context shift that would otherwise lead to model
prediction errors: first, we describe how human intuition and expert knowledge
can identify semantically meaningful features upon which models systematically
fail, second, we detail how dynamic benchmarking - with its focus on capturing
the data generation process - can promote generalizability through
corroboration, and third, we highlight that clarifying a model's limitations
can reduce unexpected errors. Robust machine learning is focused on model
performance beyond benchmarks, and as such, we consider three model organism
domains - facial expression recognition, deepfake detection, and medical
diagnosis - to highlight how implicit assumptions in benchmark tasks lead to
errors in practice. By paying close attention to the role of context,
researchers can design more comprehensive benchmarks, reduce context shift
errors, and increase generalizability.
