  Interpretability techniques are valuable for helping humans understand and
oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited
novel methods for studying convolutional neural networks (CNNs) at the ImageNet
scale. The objective of the competition was to help human crowd-workers
identify trojans in CNNs. This report showcases the methods and results of four
featured competition entries. It remains challenging to help humans reliably
diagnose trojans via interpretability tools. However, the competition's entries
have contributed new techniques and set a new record on the benchmark from
Casper et al., 2023.
