  As algorithmic tools increasingly aid experts in making consequential
decisions, the need to understand the precise factors that mediate their
influence has grown commensurately. In this paper, we present a crowdsourcing
vignette study designed to assess the impacts of two plausible factors on
AI-informed decision-making. First, we examine homophily -- do people defer
more to models that tend to agree with them? -- by manipulating the agreement
during training between participants and the algorithmic tool. Second, we
considered incentives -- how do people incorporate a (known) cost structure in
the hybrid decision-making setting? -- by varying rewards associated with true
positives vs. true negatives. Surprisingly, we found limited influence of
either homophily and no evidence of incentive effects, despite participants
performing similarly to previous studies. Higher levels of agreement between
the participant and the AI tool yielded more confident predictions, but only
when outcome feedback was absent. These results highlight the complexity of
characterizing human-algorithm interactions, and suggest that findings from
social psychology may require re-examination when humans interact with
algorithms.
