Explainable AI for High Energy Physics

Black box neural networks are standard in physics research but lack transparency: The paper identifies that neural networks widely used in high energy physics operate as "black boxes" whose decision-making processes are "often intractable," making it difficult for scientists to understand how these models reach their conclusions.

Explainable AI can make physics models more interpretable: xAI methods can establish "a quantitative and tractable relationship between the input and the model's output," potentially transforming opaque models into systems whose reasoning can be understood and verified by researchers.

Interpretability matters for scientific credibility: By making neural networks' inner workings transparent, explainable AI helps build trust in AI-driven physics research by allowing scientists to validate that models are learning physically meaningful relationships rather than exploiting spurious patterns in data.

xAI bridges the gap between accuracy and understanding: The paper suggests xAI methods can help physics researchers determine a model's "relationship with data," addressing a key tension in AI adoption where highly accurate neural networks often sacrifice interpretability—a critical concern in scientific contexts where understanding why a model works is as important as that it works.