人工介入用户反馈影响感知准确性和信任度，但任务主观性起着关键作用

While machine learning can produce complex models beyond those that a human could produce manually, incorporating human input can often improve intelligent system performance beyond purely data-driven models. These human-in-the-loop approaches can also be used to dynamically update a model in the presence of shifting data or system goals. While this feedback could come from system designers or domain experts, in many cases, the end users who regularly use the system will naturally develop an understanding of its flaws and desire the ability to change the system’s behavior based on their knowle