Addressing Bias in Visualization Recommenders by Identifying Trends in Training Data: Improving VizML Through a Statistical Analysis of the Plotly Community Feed

TL;DR

This paper investigates how biases in training data affect visualization recommendation systems and proposes a statistical analysis approach to identify and mitigate these biases, improving model fairness and performance.

Contribution

It introduces a method for analyzing training data trends to address bias in visualization recommenders, enhancing their reliability and fairness.

Findings

Identified prevalent biases in the Plotly community feed data.

Demonstrated that bias analysis can improve recommendation fairness.

Provided insights into data-driven bias mitigation strategies.

Abstract

Machine learning is a promising approach to visualization recommendation due to its high scalability and representational power. Researchers can create a neural network to predict visualizations from input data by training it over a corpus of datasets and visualization examples. However, these machine learning models can reflect trends in their training data that may negatively affect their performance. Our research project aims to address training bias in machine learning visualization recommendation systems by identifying trends in the training data through statistical analysis.