  The use of machine learning or artificial intelligence (ML/AI) holds
substantial potential toward improving many functions and needs of the public
sector. In practice however, integrating ML/AI components into public sector
applications is severely limited not only by the fragility of these components
and their algorithms, but also because of mismatches between components of
ML-enabled systems. For example, if an ML model is trained on data that is
different from data in the operational environment, field performance of the ML
component will be dramatically reduced. Separate from software engineering
considerations, the expertise needed to field an ML/AI component within a
system frequently comes from outside software engineering. As a result,
assumptions and even descriptive language used by practitioners from these
different disciplines can exacerbate other challenges to integrating ML/AI
components into larger systems. We are investigating classes of mismatches in
ML/AI systems integration, to identify the implicit assumptions made by
practitioners in different fields (data scientists, software engineers,
operations staff) and find ways to communicate the appropriate information
explicitly. We will discuss a few categories of mismatch, and provide examples
from each class. To enable ML/AI components to be fielded in a meaningful way,
we will need to understand the mismatches that exist and develop practices to
mitigate the impacts of these mismatches.
