Human-in-the-loop (HIL) systems have emerged as a promising approach for combining the strengths of data-driven machine learning models with the contextual understanding of human experts. However, a deeper look into several of these systems reveals that calling them HIL would be a misnomer, as they are quite the opposite, namely AI-in-the-loop ($AI^2L$) systems, where the human is in control of the system, while the AI is there to support the human. We argue that existing evaluation methods often overemphasize the machine (learning) component's performance, neglecting the human expert's critical role. Consequently, we propose an $AI^2L$ perspective, which recognizes that the human expert is an active participant in the system, significantly influencing its overall performance. By adopting an $AI^2L$ approach, we can develop more comprehensive systems that faithfully model the intricate interplay between the human and machine components, leading to more effective and robust AI systems.

翻译：人在回路（HIL）系统已成为一种前景广阔的方法，它将数据驱动的机器学习模型的优势与人类专家的情境理解能力相结合。然而，深入审视其中一些系统会发现，将其称为HIL实属用词不当，因为它们恰恰相反，是AI在回路（$AI^2L$）系统——即由人类掌控系统，而AI则在此为人类提供支持。我们认为，现有的评估方法往往过度强调机器（学习）组件的性能，忽视了人类专家的关键作用。因此，我们提出一种$AI^2L$视角，该视角承认人类专家是系统中的积极参与者，显著影响着系统的整体性能。通过采用$AI^2L$方法，我们可以开发出更全面的系统，忠实地建模人机组件之间复杂的相互作用，从而构建更有效、更稳健的AI系统。