Lessons on interpretable machine learning from particle physics - Grojean, Christophe; Paul, Ayan; Qian, Zhuoni; Strümke, Inga (Nature reviews physics, 2022)
Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Christophe Grojean, Ayan Paul, Zhuoni Qian and Inga Strümke give an overview of how to introduce interpretability to methods commonly used in particle physics.