Many researchers motivate explainable AI with studies showing that human-AI
team performance on decision-making tasks improves when the AI explains its
recommendations. However, prior studies observed improvements from explanations
only when the AI, alone, outperformed both the human and the best team. Can
explanations help lead to complementary performance, where team accuracy is
higher than either the human or the AI working solo? We conduct mixed-method
user studies on three datasets, where an AI with accuracy comparable to humans
helps participants solve a task (explaining itself in some conditions). While
we observed complementary improvements from AI augmentation, they were not
increased by explanations. Rather, explanations increased the chance that
humans will accept the AI's recommendation, regardless of its correctness. Our
result poses new challenges for human-centered AI: Can we develop explanatory
approaches that encourage appropriate trust in AI, and therefore help generate
(or improve) complementary performance?

研究表明，仅当 AI 单独胜过人类和最佳团队时，AI 解释才能提高人类 - AI 团队在决策任务上的表现。本研究通过三个数据集上的混合法用户研究发现，AI 表现增强是有可能提高团队准确性的，但并非由于解释的作用，而是由于解释能提高人类对 AI 建议的接受率，进而鼓励恰当的信任和生成更好的绩效。