Before revision:  We show that explanations increase the chance that humans will accept the AI's recommendation regardless of whether the AI is correct. While this clarifies the gains in team performance from explanations in prior work, it poses new challenges for human-centered AI: how can we best design systems to produce complementary performance?
After revision:  We show that explanations increase the chance that humans will accept the AI's recommendation , regardless of its correctness. Our result poses new challenges for human-centered AI: how can we best design systems to produce complementary performance?