  In this work, we empirically examine human-AI decision-making in the presence
of explanations based on predicted outcomes. This type of explanation provides
a human decision-maker with expected consequences for each decision alternative
at inference time - where the predicted outcomes are typically measured in a
problem-specific unit (e.g., profit in U.S. dollars). We conducted a pilot
study in the context of peer-to-peer lending to assess the effects of providing
predicted outcomes as explanations to lay study participants. Our preliminary
findings suggest that people's reliance on AI recommendations increases
compared to cases where no explanation or feature-based explanations are
provided, especially when the AI recommendations are incorrect. This results in
a hampered ability to distinguish correct from incorrect AI recommendations,
which can ultimately affect decision quality in a negative way.
