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