Revisiting Rashomon: A Comment on "The Two Cultures"

TL;DR

This paper discusses the Rashomon Effect in machine learning, where multiple models achieve similar accuracy but differ significantly in their data processing, complicating interpretation and decision-making.

Contribution

It offers reflections on the Rashomon Effect, connecting it to recent ML research and highlighting its importance for collaboration between different modeling cultures.

Findings

Multiple models can fit data equally well but differ in their internal processes.

The Rashomon Effect impacts interpretability and decision-making in ML.

Encourages collaboration between algorithmic and data modeling approaches.

Abstract

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.