  While a vast collection of explainable AI (XAI) algorithms have been
developed in recent years, they are often criticized for significant gaps with
how humans produce and consume explanations. As a result, current XAI
techniques are often found to be hard to use and lack effectiveness. In this
work, we attempt to close these gaps by making AI explanations selective -- a
fundamental property of human explanations -- by selectively presenting a
subset from a large set of model reasons based on what aligns with the
recipient's preferences. We propose a general framework for generating
selective explanations by leveraging human input on a small sample. This
framework opens up a rich design space that accounts for different selectivity
goals, types of input, and more. As a showcase, we use a decision-support task
to explore selective explanations based on what the decision-maker would
consider relevant to the decision task. We conducted two experimental studies
to examine three out of a broader possible set of paradigms based on our
proposed framework: in Study 1, we ask the participants to provide their own
input to generate selective explanations, with either open-ended or
critique-based input. In Study 2, we show participants selective explanations
based on input from a panel of similar users (annotators). Our experiments
demonstrate the promise of selective explanations in reducing over-reliance on
AI and improving decision outcomes and subjective perceptions of the AI, but
also paint a nuanced picture that attributes some of these positive effects to
the opportunity to provide one's own input to augment AI explanations. Overall,
our work proposes a novel XAI framework inspired by human communication
behaviors and demonstrates its potentials to encourage future work to better
align AI explanations with human production and consumption of explanations.
