Time series anomaly detection is a critical machine learning task for
numerous applications, such as finance, healthcare, and industrial systems.
However, even high-performed models may exhibit potential issues such as
biases, leading to unreliable outcomes and misplaced confidence. While model
explanation techniques, particularly visual explanations, offer valuable
insights to detect such issues by elucidating model attributions of their
decision, many limitations still exist -- They are primarily instance-based and
not scalable across dataset, and they provide one-directional information from
the model to the human side, lacking a mechanism for users to address detected
issues. To fulfill these gaps, we introduce HILAD, a novel framework designed
to foster a dynamic and bidirectional collaboration between humans and AI for
enhancing anomaly detection models in time series. Through our visual
interface, HILAD empowers domain experts to detect, interpret, and correct
unexpected model behaviors at scale. Our evaluation with two time series
datasets and user studies demonstrates the effectiveness of HILAD in fostering
a deeper human understanding, immediate corrective actions, and the reliability
enhancement of models.

我们介绍了 HILAD，这是一个旨在提升时间序列异常检测模型的人工智能与人类之间动态双向协作的新框架，通过我们的可视化界面，HILAD 赋予领域专家在大规模上检测、解释和纠正意外的模型行为的能力，通过两个时间序列数据集的评估和用户研究，我们证明了 HILAD 在促进深入人类理解、立即纠正行动以及模型可靠性提升方面的有效性。