# ViCE: Visual Counterfactual Explanations for Machine Learning Models

arXiv: https://arxiv.org/abs/2003.02428 · HTML: https://mlsanity.com/paper/2003.02428/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 30 of 34)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications](https://mlsanity.com/paper/2103.04244/) (2021)
- [ECINN: Efficient Counterfactuals from Invertible Neural Networks](https://mlsanity.com/paper/2103.13701/) (2021)
- [Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations](https://mlsanity.com/paper/2302.10671/) (2023)
- [Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making](https://mlsanity.com/paper/2308.04375/) (2023)
- [A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations](https://mlsanity.com/paper/2106.02605/) (2021)
- [DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps](https://mlsanity.com/paper/2205.06935/) (2022)
- [Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions](https://mlsanity.com/paper/2111.01235/) (2021)
- [GAM Coach: Towards Interactive and User-centered Algorithmic Recourse](https://mlsanity.com/paper/2302.14165/) (2023)
- [Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making](https://mlsanity.com/paper/2401.05840/) (2024)
- [Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration](https://mlsanity.com/paper/2404.05270/) (2024)
- [AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation](https://mlsanity.com/paper/2109.05629/) (2021)
- [MCCE: Monte Carlo sampling of realistic counterfactual explanations](https://mlsanity.com/paper/2111.09790/) (2021)
- [Counterfactual Explanations via Latent Space Projection and Interpolation](https://mlsanity.com/paper/2112.00890/) (2021)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations](https://mlsanity.com/paper/2310.01684/) (2023)
- [COIN: Counterfactual Image Generation for VQA Interpretation](https://mlsanity.com/paper/2201.03342/) (2022)
- [Explaining the Road Not Taken](https://mlsanity.com/paper/2103.14973/) (2021)
- [Augmenting Decision Making via Interactive What-If Analysis](https://mlsanity.com/paper/2109.06160/) (2021)
- [Interpretable Selective Learning in Credit Risk](https://mlsanity.com/paper/2209.10127/) (2022)
- [Visualization for Recommendation Explainability: A Survey and New Perspectives](https://mlsanity.com/paper/2305.11755/) (2023)
- [Calculating and Visualizing Counterfactual Feature Importance Values](https://mlsanity.com/paper/2306.06506/) (2023)
- [Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction](https://mlsanity.com/paper/2507.02920/) (2025)
- [Leveraging Contextual Counterfactuals Toward Belief Calibration](https://mlsanity.com/paper/2307.06513/) (2023)
- [MiMICRI: Towards Domain-centered Counterfactual Explanations of Cardiovascular Image Classification Models](https://mlsanity.com/paper/2404.16174/) (2024)
- [Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions](https://mlsanity.com/paper/2505.01192/) (2025)
- [Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace Adjustments](https://mlsanity.com/paper/2602.13784/) (2026)
- [Trinary Tools for Continuously Valued Binary Classifiers](https://mlsanity.com/paper/2204.08136/) (2022)
- [Visual Analytics for Fine-grained Text Classification Models and Datasets](https://mlsanity.com/paper/2403.15492/) (2024)

## Abstract (arXiv)

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.