While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting scientific inquiry. In this application paper we present the results of utilizing an alternative approach that situates the mathematical framing of machine learning based anomaly detection within a participatory design framework. In a collaboration with NASA scientists working with the PIXL instrument studying Martian planetary geochemistry as a part of the search for extra-terrestrial life; we report on over 18 months of in-context user research and co-design to define the key problems NASA scientists face when looking to detect and interpret spectral anomalies. We address these problems and develop a novel spectral anomaly detection toolkit for PIXL scientists that is highly accurate while maintaining strong transparency to scientific interpretation. We also describe outcomes from a yearlong field deployment of the algorithm and associated interface. Finally we introduce a new design framework which we developed through the course of this collaboration for co-creating anomaly detection algorithms: Iterative Semantic Heuristic Modeling of Anomalous Phenomena (ISHMAP), which provides a process for scientists and researchers to produce natively interpretable anomaly detection models. This work showcases an example of successfully bridging methodologies from AI and HCI within a scientific domain, and provides a resource in ISHMAP which may be used by other researchers and practitioners looking to partner with other scientific teams to achieve better science through more effective and interpretable anomaly detection tools.

翻译：异常检测是众多科学领域中最重要的有价值问题之一，然而异常检测研究往往聚焦于可能缺乏科学探究所必需的细微差别和可解释性的人工智能方法。在本应用论文中，我们展示了采用替代方法的结果：将基于机器学习的异常检测的数学框架置于参与式设计框架内。通过与研究火星行星地球化学的PIXL仪器（作为寻找地外生命的一部分）合作的NASA科学家协作，我们报告了超过18个月的语境用户研究和协同设计，以定义NASA科学家在检测和解释光谱异常时面临的关键问题。我们针对这些问题，为PIXL科学家开发了一种新型光谱异常检测工具包，该工具包在保持高度准确性的同时，对科学解释具有强透明性。我们还描述了一整年现场部署算法及相关界面的成果。最后，我们介绍了一个通过本次合作开发的用于共同创建异常检测算法的新设计框架：迭代语义启发式异常现象建模（ISHMAP），它提供了一种过程，使科学家和研究人员能够生成原生可解释的异常检测模型。这项工作展示了在科学领域内成功桥接AI与HCI方法的范例，并提供了ISHMAP资源，可供其他研究人员和实践者用于与科学团队合作，通过更有效和可解释的异常检测工具实现更好的科学成果。