Artificial intelligence (AI) is broadly deployed as an advisor to human decision-makers: AI recommends a decision and a human accepts or rejects the advice. This approach, however, has several limitations: People frequently ignore accurate advice and rely too much on inaccurate advice, and their decision-making skills may deteriorate over time. Here, we compare the AI-as-advisor approach to the hybrid confirmation tree (HCT), an alternative strategy that preserves the independence of human and AI judgments. The HCT elicits a human judgment and an AI judgment independently of each other. If they agree, that decision is accepted. If not, a second human breaks the tie. For the comparison, we used 10 datasets from various domains, including medical diagnostics and misinformation discernment, and a subset of four datasets in which AI also explained its decision. The HCT outperformed the AI-as-advisor approach in all datasets. The HCT also performed better in almost all cases in which AI offered an explanation of its judgment. Using signal detection theory to interpret these results, we find that the HCT outperforms the AI-as-advisor approach because people cannot discriminate well enough between correct and incorrect AI advice. Overall, the HCT is a robust, accurate, and transparent alternative to the AI-as-advisor approach, offering a simple mechanism to tap into the wisdom of hybrid crowds.

翻译：人工智能（AI）被广泛部署为人类决策者的顾问：AI推荐决策，人类接受或拒绝该建议。然而，这种方法存在若干局限性：人们经常忽视准确建议，过度依赖不准确建议，且其决策能力可能随时间退化。在此，我们将“AI作为顾问”方法与混合确认树（HCT）进行比较，后者是一种保留人类与AI判断独立性的替代策略。HCT要求人类与AI分别独立做出判断：若两者结果一致，则采纳该决策；若不一致，则由第二位人类打破平局。我们使用了涵盖医疗诊断与虚假信息识别等领域的10个数据集进行比较，并在其中4个包含AI决策解释的数据子集中进行验证。结果表明，HCT在所有数据集中均优于“AI作为顾问”方法。即使在AI提供判断解释的绝大多数情况下，HCT也表现更佳。通过信号检测理论解析这些结果，我们发现HCT优于“AI作为顾问”方法的原因是：人类无法准确区分AI建议的正确与错误。总体而言，HCT作为“AI作为顾问”方法的稳健、准确且透明的替代方案，提供了利用混合群体智慧的简单机制。