The SaTML '24 CNN Interpretability Competition: New Innovations for
  Concept-Level Interpretability
Stephen Casper Jieun Yun Joonhyuk Baek Yeseong Jung Minhwan Kim Kiwan Kwon Saerom Park Hayden Moore David Shriver Marissa Connor Keltin Grimes Angus Nicolson Arush Tagade Jessica Rumbelow Hieu Minh Nguyen Dylan Hadfield-Menell
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.