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Found 13 papers, 8 papers with code
State of the Art of Visual Analytics for eXplainable Deep Learning
The survey concludes by identifying future research challenges and bridging activities that are helpful to strengthen the role of Visual Analytics as effective support for eXplainable Deep Learning and to foster the adoption of Visual Analytics solutions in the eXplainable Deep Learning community.
Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Sets
We also contribute SuRE, a visual analytics (VA) system that integrates HSR and interactive surrogate rule visualizations.
An Exploration And Validation of Visual Factors in Understanding Classification Rule Sets
Rule sets are typically presented as a text-based list of logical statements (rules).
AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation
Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making.
Visualizing Rule Sets: Exploration and Validation of a Design Space
Rule sets are typically presented as a text-based list of logical statements (rules).
Towards Ground Truth Explainability on Tabular Data
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines
In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines.
Human-Computer Interaction
Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs
As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works.
ViCE: Visual Counterfactual Explanations for Machine Learning Models
The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application.
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.
RuleMatrix: Visualizing and Understanding Classifiers with Rules
With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable.
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions.
Using Visual Analytics to Interpret Predictive Machine Learning Models
It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power.
