  Despite impressive performance in many benchmark datasets, AI models can
still make mistakes, especially among out-of-distribution examples. It remains
an open question how such imperfect models can be used effectively in
collaboration with humans. Prior work has focused on AI assistance that helps
people make individual high-stakes decisions, which is not scalable for a large
amount of relatively low-stakes decisions, e.g., moderating social media
comments. Instead, we propose conditional delegation as an alternative paradigm
for human-AI collaboration where humans create rules to indicate trustworthy
regions of a model. Using content moderation as a testbed, we develop novel
interfaces to assist humans in creating conditional delegation rules and
conduct a randomized experiment with two datasets to simulate in-distribution
and out-of-distribution scenarios. Our study demonstrates the promise of
conditional delegation in improving model performance and provides insights
into design for this novel paradigm, including the effect of AI explanations.
