  High-stakes prediction tasks (e.g., patient diagnosis) are often handled by
trained human experts. A common source of concern about automation in these
settings is that experts may exercise intuition that is difficult to model
and/or have access to information (e.g., conversations with a patient) that is
simply unavailable to a would-be algorithm. This raises a natural question
whether human experts add value which could not be captured by an algorithmic
predictor. We develop a statistical framework under which we can pose this
question as a natural hypothesis test. Indeed, as our framework highlights,
detecting human expertise is more subtle than simply comparing the accuracy of
expert predictions to those made by a particular learning algorithm. Instead,
we propose a simple procedure which tests whether expert predictions are
statistically independent from the outcomes of interest after conditioning on
the available inputs (`features'). A rejection of our test thus suggests that
human experts may add value to any algorithm trained on the available data, and
has direct implications for whether human-AI `complementarity' is achievable in
a given prediction task. We highlight the utility of our procedure using
admissions data collected from the emergency department of a large academic
hospital system, where we show that physicians' admit/discharge decisions for
patients with acute gastrointestinal bleeding (AGIB) appear to be incorporating
information that is not available to a standard algorithmic screening tool.
This is despite the fact that the screening tool is arguably more accurate than
physicians' discretionary decisions, highlighting that -- even absent normative
concerns about accountability or interpretability -- accuracy is insufficient
to justify algorithmic automation.
