  As AI models evolve, understanding the influence of underlying models on user
experience and performance in AI-infused systems becomes critical, particularly
while transitioning between different model versions. We studied the influence
of model change by conducting two complementary studies in the context of
AI-based facial recognition for historical person identification tasks. First,
we ran an online experiment where crowd workers interacted with two different
facial recognition models: an older version and a recently updated,
developer-certified more accurate model. Second, we studied a real-world
deployment of these models on a popular historical photo platform through a
diary study with 10 users. Our findings sheds light on models affecting
human-AI team performance, users' abilities to differentiate between different
models, the folk theories they develop, and how these theories influence their
preferences. Drawing from these insights, we discuss design implications for
updating models in AI-infused systems.
