论文标题
人类AI团队的个性化：改善兼容性 - 准确性权衡
Personalization in Human-AI Teams: Improving the Compatibility-Accuracy Tradeoff
论文作者
论文摘要
建模并与用户交互的AI系统可以随着时间的推移更新其模型，以反映环境中的新信息和变化。尽管这些更新可能会改善AI系统的整体性能，但实际上它们可能会损害个人用户的性能。先前的工作研究了更新后改善系统准确性的权衡与更新系统与先前的用户体验的兼容性之间的权衡。该模型被迫与先前版本兼容的越多，其准确性损失就会越高。在本文中，我们表明，通过个性化特定用户的损失功能，在某些情况下，可以改善相对于这些用户的兼容性准确性权衡（增加模型的兼容性，同时牺牲较低的准确性）。我们提出了实验结果，表明这种方法平均提供了适度的改进（约为20％），但对某些用户（最高300％）提供了大量改进。
AI systems that model and interact with users can update their models over time to reflect new information and changes in the environment. Although these updates may improve the overall performance of the AI system, they may actually hurt the performance with respect to individual users. Prior work has studied the trade-off between improving the system's accuracy following an update and the compatibility of the updated system with prior user experience. The more the model is forced to be compatible with a prior version, the higher loss in accuracy it will incur. In this paper, we show that by personalizing the loss function to specific users, in some cases it is possible to improve the compatibility-accuracy trade-off with respect to these users (increase the compatibility of the model while sacrificing less accuracy). We present experimental results indicating that this approach provides moderate improvements on average (around 20%) but large improvements for certain users (up to 300%).
