Don't worry, researchers will just get an AI to interpret all those floating point numbers and come up with a human-readable explanation! What could go wrong? /s

Hey look, this took me like 5 minutes to find.

Censius guide to AI interpretability tools

Here's a good thing to wonder: if you don't know how you're black box model works, how do you know it isn't racist?

Here's what looks like a university paper on interpretability tools:

As a practical example, new regulations by the European Union proposed that individuals affected by algorithmic decisions have a right to an explanation. To allow this, algorithmic decisions must be explainable, contestable, and modifiable in the case that they are incorrect.

Oh yeah. I forgot about that. I hope your model is understandable enough that it doesn't get you in trouble with the EU.

Oh look, here you can actually see one particular interpretability tool being used to interpret one particular model. Funny that, people actually caring what their models are using to make decisions.

Look, maybe you were having a bad day, or maybe slapping people is literally your favorite thing to do, who am I to take away mankind's finer pleasures, but this attitude of yours is profoundly stupid. It's weak. You don't want to know? It doesn't make you curious? Why are you comfortable not knowing things? That's not how science is propelled forward.

Well in theory you can explain how the model comes to it's conclusion. However I guess that 0.1% of the "AI Engineers" are actually capable of that. And those costs probably 100k per month.

Link?

It depends on the algorithms used. Now the lazy approach is to just throw neural networks at everything and waste immense computation ressources. Of course you then get results that are difficult to interpret. There is much more efficient algorithms that are working well to solve many problems and give you interpretable decisions.

The AI developers understand how AI works, but that does not mean that they understand the thing that the AI is trained to detect.

For instance, the cutting edge in protein folding (at least as of a few years ago) is Google's AlphaFold. I'm sure the AI researchers behind AlphaFold understand AI and how it works. And I am sure that they have an above average understanding of molecular biology. However, they do not understand protein folding better than the physisits and chemists who have spent their lives studying the field. The core of their understanding is "the answer is somewhere in this dataset. All we need to do is figure out how to through ungoddly amounts of compute at it, and we can make predictions". Working out how to productivly throw that much compute power at a problem is not easy either, and that is what ML researchers understand and are experts in.

In the same way, the researchers here understand how to go from a large dataset of breast images to cancer predictions, but that does not mean they have any understanding of cancer. And certainly not a better understanding than the researchers who have spent their lives studying it.

An open problem in ML research is how to take the billions of parameters that define an ML model and extract useful information that can provide insights to help human experts understand the system (both in general, and in understanding the reasoning for a specific classification). Progress has been made here as well, but it is still a long way from being solved.

What a vague and unprovable thing you've stated there.