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:尽管异常探测是许多科学领域最重要和最宝贵的问题之一,但异常探测研究往往侧重于对科学调查至关重要的缺乏精细度和解释的AI方法。在本应用文件中,我们介绍了使用一种替代方法的结果,这种方法将机器学习异常现象探测的数学框架置于参与性设计框架之内。我们与美国航天局科学家与研究火星行星地球化学学的PIXL仪器合作,研究火星行星地球化学学,以此作为寻找地球生命的一部分;我们报告18个多月的内文本用户研究和共同设计,以确定美国航天局科学家在期待探测和解释光谱异常时所面临的关键问题。我们处理这些问题,并为PIXL科学家开发了一个新的光谱异常探测工具包,该工具包在保持科学解释的高度透明度的同时,又非常精确。我们还介绍了长期实地部署算法和相关界面的结果。 最后,我们引入了一个新的设计框架,这是我们通过这一合作过程开发的可共同生成的异常探测算法工具:Anomalio Phenomena(ISMAP) 的超常理学模型(ISMAP) 的超常建模(ISMAD) 的超常性超常建模模型(ISMAD) 的超常性超常模拟模型(HISM) 模型(HISMAD) 和超常地模型(HISL),它提供了一种可复制的原科学探测方法,为研究人员提供了一种可复制的原科学探测方法,在实验室的模型,从而提供一种可复制工具,在实验室的模型,在实验室中提供一种可复制的模型,为DNA探测方法,在实验室内进行中提供一种可复制的模型,从而提供一种可复制的模型,从而提供一种可复制的模型。