  Machine learning models are increasingly integrated into societally critical
applications such as recidivism prediction and medical diagnosis, thanks to
their superior predictive power. In these applications, however, full
automation is often not desired due to ethical and legal concerns. The research
community has thus ventured into developing interpretable methods that explain
machine predictions. While these explanations are meant to assist humans in
understanding machine predictions and thereby allowing humans to make better
decisions, this hypothesis is not supported in many recent studies. To improve
human decision-making with AI assistance, we propose future directions for
closing the gap between the efficacy of explanations and improvement in human
performance.
