Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time

Understand

Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning.

• In computer vision, prototypical part models (ProtoPNets) have become the dominant model type to meet this need.
• Users can easily identify flaws in ProtoPNets, but fixing problems in a ProtoPNet requires slow, difficult retraining that is not guaranteed to resolve the issue.
• This problem is called the "interaction bottleneck." We solve the interaction bottleneck for ProtoPNets by simultaneously finding many equally good ProtoPNets (i.e., a draw from a "Rashomon set").