How can human-AI teams outperform both AI alone and human alone, i.e. achieving complementary performance? In our new paper presenting at #CSCW2021, we propose two directions: out-of-distribution examples and interactive explanations. Here’s a thread about these new perspectives: 1/n First, prior work adopts an over-optimistic scenario for AI: test set follows the same distribution as training set (in-distribution). In practice, examples during testing may differ substantially from training, and AI performance can drop significantly (out-of-distribution).