Existing user studies suggest that different tasks may require explanations with different properties. However, user studies are expensive. In this paper, we introduce a generalizable, cost-effective method for identifying task-relevant explanation properties in silico, which can guide the design of more expensive user studies. We use our approach to identify relevant proxies for three example tasks and validate our simulation with real user studies.

翻译：现有用户研究表明，不同任务可能需要具有不同属性的解释。然而，用户研究成本高昂。本文提出一种通用且经济高效的方法，可在计算机模拟环境中识别任务相关的解释属性，从而为设计更复杂的用户研究提供指导。我们运用该方法为三个示例任务识别相关代理指标，并通过真实用户研究验证了仿真结果的有效性。