Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics

Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: Next we'll be talking about the paper "Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics".

Jocelyn: The paper was written by Rikab Gambhir, Luisa Lucie-Smith and Jesse Thaler from University of Cincinnati and Universität Hamburg and Massachusetts Institute of Technology and Institut des Hautes Études Scientifiques and CEA Paris-Saclay and The NSF Institute for Artificial Intelligence and Fundamental Interactions.

Vera: Stay tuned as we take you through the paper and discuss its implications.

Paper discussion segment 1: Vera: We are moving from our look at specific cosmic observations into a more foundational discussion about how we actually process information in modern physics. We are looking at a very important new piece titled 'Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics' by Rikab Gambhir, Luisa Lucie-Smith, and Jesse Thaler. It is part of this larger VERaiPHY initiative which is basically trying to set some ground rules for how we use AI in things like high energy physics and cosmology.

Jocelyn: It sounds like they are finally addressing the elephant in the room, doesn't it? For a long time, people have just been throwing these massive neural networks at data and hoping for the best.

Vera: Exactly, and these authors are coming from some heavy-hitting institutions like MIT and the University of Cincinnati to say we can't just treat these models as magic boxes anymore. They are arguing that if we want to do real science, we have to stop using these terms like "transparency" or "trustworthiness" interchangeably because they mean very different things when you are trying to understand a galaxy or a particle collision.

Subrahmanyan: I think it is crucial that they are bringing this up now, especially as these models get larger and more complex. If we cannot distinguish between the structure of the math and the physical meaning of the result, we are essentially doing statistics without any connection to reality. It is one thing to have a model that predicts a number correctly, but it is quite another to know if that number actually tells us something about the universe.

Jocelyn: That distinction between the math and the physics seems to be their main starting point. They aren't just talking about computer science; they are talking about the philosophy of how a physicist works.

Subrahmanyan: Precisely, Jocelyn. They are suggesting that machine learning models should be held to the same rigorous standards as our classical models, like General Relativity or the Standard Model. The only real difference is the scale and complexity, not the fundamental requirement for understanding.

Vera: It is a bold claim to say that a neural network is just a "classical" model in a different skin, but it sets the stage for how they define their terms. Let's look at how they actually draw those lines between interpretability and explainability.

Paper discussion segment 2: Jocelyn: Now that we have the context of who is writing this, let's get into what 'Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics' actually proposes as its core framework. They make this very sharp distinction that I think will change how researchers approach their model design.

Vera: Right, they define interpretability as being about the structural transparency of the model itself. It is basically asking if we can look at the inner workings, the actual computation or the "mechanics," and understand how an input turns into an output. It is a question of form and structure rather than what that structure actually represents in our physical world.

Subrahmanyan: And then there is explainability, which they treat as a completely different axis. Explainability isn't about the math; it's about whether we can map those mathematical results onto our existing domain knowledge, like laws of gravity or particle interactions. You could have a model that is perfectly interpretable because you can see every single multiplication happening, but if those multiplications don't correspond to a physical concept, then it isn't explainable at all.

Jocelyn: That is such an important point because it explains why we often feel so lost with deep learning. We might be able to see the code and the weights, which makes it somewhat interpretable in a technical sense, but we have no idea how those weights relate to, say, the density of dark matter.

Vera: They even use a quadrant system to show that you can have one without the other. You could have something that is explainable but not interpretable—like a complex mathematical function where we know the physical result but the actual numerical computation is a total black box.

Subrahmanyan: Or you can have the most dangerous case: something that is neither. If a model is both opaque in its structure and disconnected from physical principles, it provides no scientific value; it's just a tool for prediction without any capacity for insight.

Jocelyn: It really forces you to realize that "understanding" isn't just one single thing. Let's talk about the trade-offs they mention, because you can't just have everything at once.

Paper discussion segment 3: Vera: We are digging into the practical reality of these definitions now, specifically looking at the costs associated with them in 'Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics'. The authors point out that there is no free lunch here; every choice you make has a direct consequence for your science.

Jocelyn: They frame it as a tension between interpretability and expressivity, and then between explainability and adaptability. If you want a model that is incredibly easy to interpret, like a simple linear regression or a basic parametric fit, you are going to lose the ability to capture the incredibly complex, messy patterns found in real cosmic data.

Subrahmanyan: That is the fundamental struggle of modeling. If you constrain your model too much so that it's easy for a human to read, you might miss the very phenomena you are looking for because your model was too simple to "express" them. On the other hand, if you let a neural network be as expressive as it wants, it will fit the data perfectly, but it will be an uninterpretable mess that tells you nothing about the underlying physics.

Vera: And then they bring up adaptability, which is tied to explainability. If you force a model to adhere strictly to known physical laws—like making sure a model respects Lorentz invariance—you are increasing its explainability, but you might be limiting its ability to adapt to new data that suggests our current laws are incomplete.

Jocelyn: It's like the difference between a rigid theory and an empirical fit. One is deeply connected to what we know, but it might be too stiff; the other is flexible, but it might just be memorizing noise.

Subrahmanyan: They also suggest that we need to be much more intentional about our "intervention plans." It isn't enough to just run a post-hoc test like a Shapley value and say, "look, this feature is important." You have to decide beforehand what you would actually do with that information if it turned out to be unexpected.

Vera: That's a great point about the methodology. They suggest moving toward "intrinsic" methods where we build the physics directly into the architecture, rather than just trying to explain it after the fact.

Conclusion: Vera: We have covered a lot of ground today, from the basic definitions to the difficult trade-offs that every physicist using AI has to face. This paper really pushes us to be more disciplined in how we design our experiments and our models.

Jocelyn: It really does. It's not just about getting a better accuracy score on a leaderboard; it's about ensuring that the work we do actually contributes to our collective understanding of the cosmos.

Subrahmanyan: I think the most vital takeaway is that machine learning isn't a separate branch of science that operates by different rules. It is simply a new way of building models, and it must be integrated into our existing scientific framework with all the same skepticism and rigor we apply to any other theory.

Vera: Well said. We've spent our time today looking at 'Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics', and it has certainly given us a new lens through which to view the data coming in from our telescopes and colliders.

Jocelyn: It's a heavy topic, but a necessary one if we want to turn these black boxes into true instruments of discovery.

Subrahmanyan: I agree; the era of treating AI as a magic oracle must end if we are to enter the era of machine-driven discovery.

Vera: Thank you all for listening. We'll be back next time with another look at what's happening on the frontiers of research. Goodbye for now.

Jocelyn: Goodbye!

Subrahmanyan: Goodbye everyone.