Revisiting Rashomon: A Comment on "The Two Cultures"
This is an incremental commentary on a known problem in machine learning, relevant for researchers dealing with model interpretability and decision-making.
The paper reflects on the Rashomon Effect, where multiple models achieve similar predictive accuracy but interpret data differently, complicating conclusions and automation. It connects this issue to recent machine learning work and suggests it as a collaborative area between algorithmic and data modeling cultures.
Here, I provide some reflections on Prof. Leo Breiman's "The Two Cultures" paper. I focus specifically on the phenomenon that Breiman dubbed the "Rashomon Effect", describing the situation in which there are many models that satisfy predictive accuracy criteria equally well, but process information in the data in substantially different ways. This phenomenon can make it difficult to draw conclusions or automate decisions based on a model fit to data. I make connections to recent work in the Machine Learning literature that explore the implications of this issue, and note that grappling with it can be a fruitful area of collaboration between the algorithmic and data modeling cultures.