论文笔记 Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations
Subject: Interactive Model Analysis
Target: Verify the performance of a model
Existing methods: statistical methods, in an aggregated fashion (e.g. accuracy)
Related work:
- White box approach: Aiming at visualizing the internal structures of the models
- Logistic Regression: transparent weighting of the features
- Black box approach
- Models comparison:
- ModelTracker
- MLCube Explorer: data cube analysis type
Contribution: a workflow and an interface
Novelty
- Focus on input/output behaviour of a model (model agnostic)
- Locally and globally, decisions and feature importance
Workflow:
Core of the explanation algorithm: Removing features from a vector until the predicted label changes.
User Interface of Rivelo
Limitations: works with binary classifiers and binary features
Useful Quotes: DARPA XAI program: “the effectiveness of these systems is limited by the machines current inability to explain their decisions and actions to human users [. . .] it is essential to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners"
Reference:
[1] Tamagnini, Paolo, et al. "Interpreting Black-Box Classifiers Using Instance-Level Visual Explanations." (2017).
