[hooande]

Debugging is a problem. But the real problem I'm seeing is our expectations as software developers. We're used to being able to fix any problem that we see. If a div is misaligned or a column of numbers is wrong we can open the file, find the offending lines of code and FIX it.

Machine learning is different because every implementation has a known error rate. If your application has a measured 80% accuracy then 20% of cases WILL have an error. You don't know which 20% and you don't get to choose. There's no way to notice a problem and immediately fix it, like you can with almost every other kind of engineering. At best you can expand your dataset, incorporate new models, fix actual bugs in the code. Doing those things could increase the accuracy up to, say, 85%. This means there will be fewer errors overall, but the one that you happened to notice may or may not still be there. There's no way to directly intervene.

I see a lot of people who are new to the field struggle with this. There are many ways to improve models and handle edge cases. But not being able to fix a problem that's in front of you takes some getting used to.