How I stopped worrying about training data bugs and started complaining

Abstract

There is an increasing awareness of the gap between machine learning research and production. The research community has largely focused on developing a model that performs well on a validation set, but the production environment needs to make sure the model also performs well in a downstream application. The latter is more challenging because the test/inference-time data used in the application could be quite different from the training data. To address this challenge, we advocate for "complaint-driven" data debugging, which allows the user to complain about the unexpected behaviors of the model in the downstream application, and proposes interventions for training data errors that likely led to the complaints. This new debugging paradigm helps solve a range of training data quality problems such as labeling error, fairness, and data drift. We present our long-term vision, highlight achieved milestones, and outline a research roadmap including a number of open problems.