AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation

TL;DR

AdViCE is a visual analytics tool that helps data scientists interpret, debug, and validate machine learning models by providing interactive decision comparisons and counterfactual explanations.

Contribution

The paper introduces AdViCE, a novel visual analytics system combining interactive decision comparison and counterfactual explanation visualization for model validation.

Findings

Enables comparison of model decisions on user-defined data subsets.

Visualizes counterfactual explanations to understand feature influence.

Demonstrates effectiveness through a practical use case.

Abstract

Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making. Meanwhile, the increased integration of these models into various application domains has further highlighted the need for greater interpretability and transparency. To identify problems such as bias, overfitting, and incorrect correlations, data scientists require tools that explain the mechanisms with which these model decisions are made. In this paper we introduce AdViCE, a visual analytics tool that aims to guide users in black-box model debugging and validation. The solution rests on two main visual user interface innovations: (1) an interactive visualization design that enables the comparison of decisions on user-defined data subsets; (2) an algorithm and visual design to compute and visualize counterfactual explanations - explanations that depict model outcomes when data features are perturbed from their original values. We provide a demonstration of the tool through a use case that showcases the capabilities and potential limitations of the proposed approach.