Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these 'ironies of automation' have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users' roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.

翻译：生成式人工智能系统为编程、写作等众多任务提供了提升用户生产力的机会。然而，尽管部分研究表明这类系统能提升生产力，但更多研究发现用户在与生成式人工智能系统协作时效率低下，甚至导致生产力损失。尽管这些可用性挑战看似新颖，但早在三十多年前，人因工程领域针对航空、自动驾驶、情报分析等自动化系统引入的研究中就已观察到此类“自动化讽刺”。我们借鉴这些丰富的研究成果，并结合近期生成式人工智能用户研究，归纳出四类导致生成式人工智能系统生产力损失的关键原因：用户角色从生产者转变为评估者、工作流程的非必要重构、交互中断，以及自动化倾向于使简单任务更易完成、困难任务更难完成。在此基础上，我们提出人因工程研究如何通过持续反馈、系统个性化、生态界面设计、任务稳定性保障及明确的任务分配等方法，指导生成式人工智能系统设计以减轻生产力损失。由此，我们将生成式人工智能系统可用性方面的进展建立在数十年人因工程研究的基础上，确保这一快速发展的领域中的人机交互设计能够从历史中汲取经验，而非重蹈覆辙。