Trying to solve a frustrating problem: why is debugging AI systems still so unclear?

I’ve been thinking about something that keeps coming up when working with AI systems. When a model gives a wrong or weird output, it’s surprisingly hard to figure out what actually went wrong. Most of the time we’re digging through logs or guessing where things broke. As part of the AWS AI Ideas hackathon, I started building a concept called AutopsyAI. The idea is to look at the full pipeline from input to output and try to explain where things might have failed, instead of just showing the final result.