Missingness Bias in Model Debugging

Understand

Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools.

• However, in computer vision, pixels cannot simply be removed from an image.
• One thus tends to resort to heuristics such as blacking out pixels, which may in turn introduce bias into the debugging process.
• We study such biases and, in particular, show how transformer-based architectures can enable a more natural implementation of missingness, which side-steps these issues and improves the reliability of model debugging in practice.