The Importance of Being Interpretable

Cosmology is entering an era of data-driven science, due in part to modern machine learning techniques that enable powerful new data analysis methods. This is a shift in our scientific approach, and requires us to ask an important question: Can we trust the black box? I will present a deep machine learning approach to constraining cosmological parameters from X-ray surveys of galaxy clusters and describe how interpreting the model led to the discovery of a previously unknown self-calibration mode for flux- and volume-limited cluster surveys.