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

• URL: http://arxiv.org/abs/2203.04937v1
• Date: Wed, 9 Mar 2022 18:36:46 GMT
• Title: Addressing Bias in Visualization Recommenders by Identifying Trends in Training Data: Improving VizML Through a Statistical Analysis of the Plotly Community Feed
• Authors: Allen Tu, Priyanka Mehta, Alexander Wu, Nandhini Krishnan, Amar Mujumdar
• Abstract summary: Machine learning is a promising approach to visualization recommendation due to its high scalability and representational power. Our research project aims to address training bias in machine learning visualization recommendation systems by identifying trends in the training data through statistical analysis.
• Score: 55.41644538483948
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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