Knowledge Trap : Human Experts Distracted by Details When Teaming with AI

In the emerging literature on human–artificial intelligence (AI) teaming, the effectiveness of teaming is established on the complementarity between the unique advantages of both human experts and AI. Due to the nature of machine learning, contemporary AI can outperform human experts in narrow and well-defined tasks. However, human experts possess broad, context-aware, and forward-looking knowledge. While the entire human domain knowledge cannot be easily modeled, AI is positioned as augmented intelligence so that complex decision-making relies on human experts for final judgment. However, the broad knowledge of human experts is not always beneficial. We conduct a field experiment that showcases a phenomenon of knowledge trap in the context of human–AI teaming, in which human experts’ overfocus on details hurts their performance compared with AI’s concentrated yet narrow focus. Based on data from our field experiment, on average, video thumbnails selected by human experts attract fewer views than those selected by AI. Even more surprisingly, human and AI teaming together performs worse than just AI. This difference can be explained by the human experts’ overfocus on the thumbnail’s coverage of video details. While such details are helpful to certain subgroups, such as long videos and videos with less information in the title, we observe an inverted U-shaped relationship between the probability of a video being viewed and the level of details in its thumbnail. Therefore, compared with AI’s sole focus on visual features, domain expertise’s overemphasis on details becomes a knowledge trap, which largely reduces the effectiveness of human–AI teaming