Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy
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DOI：
10.1609/hcomp.v8i1.7464
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Donald R. Honeycutt;Mahsan Nourani;E. Ragan
中科院分区：
文献类型：
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作者：
Donald R. Honeycutt;Mahsan Nourani;E. Ragan
混合主动系统允许用户交互式地提供反馈，以潜在地提高系统性能。人工反馈可以纠正模型错误并更新模型参数，以动态适应不断变化的数据。此外，许多用户希望能够有更高的控制水平，并修复他们所依赖的系统中的感知缺陷。然而，向自治系统提供反馈的能力如何影响用户信任，这在很大程度上是一个尚未探索的研究领域。我们的研究调查了提供反馈的行为如何影响用户对智能系统及其准确性的理解。我们提出了一个对照实验，使用模拟对象检测系统与图像数据，研究交互式反馈收集对用户印象的影响。结果表明，提供人在回路反馈降低了参与者对系统的信任和他们对系统准确性的感知，无论系统准确性是否响应他们的反馈而提高。这些结果强调了在设计智能系统时考虑最终用户反馈对用户信任的影响的重要性。
Mixed-initiative systems allow users to interactively provide feedback to potentially improve system performance. Human feedback can correct model errors and update model parameters to dynamically adapt to changing data. Additionally, many users desire the ability to have a greater level of control and fix perceived flaws in systems they rely on. However, how the ability to provide feedback to autonomous systems influences user trust is a largely unexplored area of research. Our research investigates how the act of providing feedback can affect user understanding of an intelligent system and its accuracy. We present a controlled experiment using a simulated object detection system with image data to study the effects of interactive feedback collection on user impressions. The results show that providing human-in-the-loop feedback lowered both participants’ trust in the system and their perception of system accuracy, regardless of whether the system accuracy improved in response to their feedback. These results highlight the importance of considering the effects of allowing end-user feedback on user trust when designing intelligent systems.