ViCE: visual counterfactual explanations for machine learning models

By Oscar Gomez, Steffen Holter, Jun Yuan, Enrico Bertini. 2020

108 citations recorded by the source corpus.

The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application. Yet, many decisions made with seemingly accurate models still require verification by domain experts. In addition, end-users of a model also want to understand the reasons behind specific decisions. Thus, the need for interpretability is increasingly paramount. In this paper we present an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions. Each sample is assessed to identify the minimal set of changes needed to flip the model's output. These explanations aim to provide end-users with personalized actionable insights with which to understand, and possibly contest or improve, automated decisions. The results are effectively displayed in a visual interface where counterfactual explanations are highlighted and interactive methods are provided for users to explore the data and model. The functionality of the tool is demonstrated by its application to a home equity line of credit dataset.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Information Systems and Management

In Safety, Robustness & Interpretability

Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, neutral): “One particularly impressive interactive visualization was done by Gomez et al. (2020) for a model on loan decisions.”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “…[73] E gen alg + heuristic query + data (2020.02) CEML [11–13] E grad opt/heuristic gradient/query (2020.02) MINT [92] R SAT white-box (2020.03) ViCE [68] E heuristic query (2020.03) Plausible CFs [22] E grad opt + gen alg dataset (2020.04) SEDC-T [175] E heuristic query (2020.04) MOC [49] E gen…”
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics (2021, methodology): “[58] proposed a visual analytics tool to generate counterfactual explanations by identifying the minimal set of changes needed to flip the model’s output.”