AI Hype: What Does Google's `Underspecification` Bombshell Mean For Machine Learning Credibility?

Last week, Google released "Underspecification Presents Challenges for Credibility in Modern Machine Learning", a paper that has been sending shockwaves through the Machine Learning community. The paper highlights a particularly thorny problem: even if machine learning models pass tests equally well, they don't perform equally well in the real world. The bugbears of models failing to meet testing performance in the real world have long been known, but this work is the first to publicly prove and name underspecification as a cause. However, before we talk about handling underspecification, we need to describe how machine learning models are put together, and what the problem is. This process has a core tenet that good performance on the testing sample means good performance on real-world data, barring systematic changes between testing and the real-world (called data shift or bias); for instance a model forecasting clothing sales after three months of winter learning is likely to struggle come summertime, having learned a lot about coats but very little about shorts.