  Artificial intelligence (AI) is increasingly being considered to assist human
decision-making in high-stake domains (e.g. health). However, researchers have
discussed an issue that humans can over-rely on wrong suggestions of the AI
model instead of achieving human AI complementary performance. In this work, we
utilized salient feature explanations along with what-if, counterfactual
explanations to make humans review AI suggestions more analytically to reduce
overreliance on AI and explored the effect of these explanations on trust and
reliance on AI during clinical decision-making. We conducted an experiment with
seven therapists and ten laypersons on the task of assessing post-stroke
survivors' quality of motion, and analyzed their performance, agreement level
on the task, and reliance on AI without and with two types of AI explanations.
Our results showed that the AI model with both salient features and
counterfactual explanations assisted therapists and laypersons to improve their
performance and agreement level on the task when `right' AI outputs are
presented. While both therapists and laypersons over-relied on `wrong' AI
outputs, counterfactual explanations assisted both therapists and laypersons to
reduce their over-reliance on `wrong' AI outputs by 21\% compared to salient
feature explanations. Specifically, laypersons had higher performance degrades
by 18.0 f1-score with salient feature explanations and 14.0 f1-score with
counterfactual explanations than therapists with performance degrades of 8.6
and 2.8 f1-scores respectively. Our work discusses the potential of
counterfactual explanations to better estimate the accuracy of an AI model and
reduce over-reliance on `wrong' AI outputs and implications for improving
human-AI collaborative decision-making.
