Increasingly, organizations are pairing humans with AI systems to improve decision-making and reducing costs. Proponents of human-centered AI argue that team performance can even further improve when the AI model explains its recommendations. However, a careful analysis of existing literature reveals that prior studies observed improvements due to explanations only when the AI, alone, outperformed both the human and the best human-AI team. This raises an important question: can explanations lead to complementary performance, i.e., with accuracy higher than both the human and the AI working alone? We address this question by devising comprehensive studies on human-AI teaming, where participants solve a task with help from an AI system without explanations and from one with varying types of AI explanation support. We carefully controlled to ensure comparable human and AI accuracy across experiments on three NLP datasets (two for sentiment analysis and one for question answering). While we found complementary improvements from AI augmentation, they were not increased by state-of-the-art explanations compared to simpler strategies, such as displaying the AI's confidence. 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? Can we develop explanatory approaches that help humans decide whether and when to trust AI input?

中文翻译：

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整体胜过部分吗？AI 解释对互补团队绩效的影响

越来越多的组织将人类与人工智能系统结合起来，以改进决策并降低成本。以人为中心的人工智能的支持者认为，当人工智能模型解释其建议时，团队绩效甚至可以进一步提高。然而，对现有文献的仔细分析表明，只有当人工智能单独表现优于人类和最好的人类人工智能团队时，先前的研究才会观察到由于解释而导致的改进。这就提出了一个重要的问题：解释能否带来互补的表现，即比人类和人工智能单独工作的准确度更高？我们通过设计关于人与人工智能合作的综合研究来解决这个问题，参与者在没有解释的人工智能系统的帮助下解决任务，并在不同类型的人工智能解释支持下解决任务。我们仔细控制以确保在三个 NLP 数据集（两个用于情感分析，一个用于问答）的实验中具有可比的人类和 AI 准确性。虽然我们发现 AI 增强带来了互补的改进，但与更简单的策略（例如展示 AI 的信心）相比，最先进的解释并没有增加这些改进。我们表明，无论人工智能是否正确，解释都会增加人类接受人工智能建议的机会。虽然这从先前工作的解释中阐明了团队绩效的收益，但它对以人为中心的人工智能提出了新的挑战：我们如何最好地设计系统以产生互补的绩效？我们能否开发解释性方法来帮助人类决定是否以及何时信任 AI 输入？我们如何才能最好地设计系统以产生互补的性能？我们能否开发解释性方法来帮助人类决定是否以及何时信任 AI 输入？