The challenge for the engineers at our AI startup is that deterministic testing paradigms don't adapt well to probabilistic models that are continually being retrained. As a scientist is hard to convey the acceptable range of variance and often the random change of individual predictions at the decision boundary. It's also hard debug behavioral issues that actually are systematic model failure versus those that are traditional infrastructure bugs. Often times the band aid is that build lookup tables to ensure certain behavior which in turn also underlying issues from being discovered.
Testing paradigms are either too high level or too specific. Recent work on evolving behavioral tests addresses this but it requires more manual effort and interpretation which kinda defeats to point of automated tests.
Testing paradigms are either too high level or too specific. Recent work on evolving behavioral tests addresses this but it requires more manual effort and interpretation which kinda defeats to point of automated tests.