I dont know if it's a good point to start, but you might want to take a look at the works by Daron Acemoglu and Pascual Restrepo pascual.scripts.mit.edu/research/

Thanks for your comments. I don't think that neural networks are just a form of memory (though they can store a large number of memories). For example, transformers with unbounded steps are Turing-complete direct.mit.edu/tacl/article...

I see the point of the original post, but I think it's also important to keep in mind this other aspect.

www.inference.vc/we-may-be-su...

Wednesday: Position: Rethinking Explainable Machine Learning as Applied Statistics icml.cc/virtual/2025...

Great to hear that you like it, and thank you for the feedback! I agree that stakeholders are important, although you are not going to find much about it in this paper. We might argue, though, that similar aspects with stakeholders arise in data science with large datasets, hence the analogy :)

There are many more interesting aspects to this, so take a look at our paper!

arxiv.org/abs/2402.02870

We would also be happy for questions and comments on why we got it completely wrong.😊

If you are at ICML, I will present this paper on Wed 16 Jul 4:30 in the East Exhibition Hall A-B #E-501.📍

We think the literature on explainable machine learning can learn a lot from looking at these papers!📚

As I learned from our helpful ICML reviewers, there is a lot of existing research at the intersection of machine learning and statistics that takes the matter of interpretation quite seriously.

In this framework, another way to formulate the initial problems is: For many popular explanation algorithms, it is not clear whether they have an interpretation.

Having an interpretation means that the explanation formalizes an intuitive human concept, which is a fancy philosophical way of saying that it is clear what aspect of the function the explanation describes.🧠

In addition, the way to develop explanations that are useful "in the world" is to develop explanations that have an interpretation.

This has several important implications. Most importantly, explainable machine learning has often been trying to reinvent the wheel when we already have a robust framework for discussing complex objects in the light of pressing real-world questions.

It took us a while to recognize it, but once you see it, you can't unsee it: Explainable machine learning is applied statistics for learned functions.✨

Concretely, researchers in applied statistics study complex datasets by mapping their most important properties into low-dimensional structures. Now think:

Machine learning model ~ Large dataset
Explanation algorithm ~ Summary statistics, visualization

Here comes our key realization: This question has occurred in other disciplines before, specifically in applied statistics research.