It seems like you're asking for quite a lot here. Are there any examples of common interpretable ML methods that would give you anything like that answer? The m

It seems like you're asking for quite a lot here. Are there any examples of common interpretable ML methods that would give you anything like that answer? The most common methods that are called interpretable would give you hints like "Mass and distance matter for gravity, color doesn't" or "Gravity gets stronger with mass and weaker with distance". Both are clearly less informative than the formula. The only way I could think of to get anywhere near such an answer would be to use symbolic regression first and then ask an LLM to interpret the result. And that would probably take quite some more original research to get it anywhere near working, and even then probably primarily for problems where the answer is already known. I agree that this kind of answer would be useful, but we also have to be honest that that's not what currently meant by interpretability. And that's what should matter for evaluating the claim - it's not misleading if it delivers what one can reasonably expect. Whether we should update our interpretability definitions is a different (interesting) discussion.