research areaHuman-Computer Interactionconference CHI
content type paperpublished April 2020

Understanding and Visualizing Data Iteration in Machine Learning

In collaboration with Carnegie Mellon University, Georgia Institute of Technology

AuthorsFred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, Kayur Patel

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Successful machine learning (ML) applications require iterations on both modeling and the underlying data. While prior visualization tools for ML primarily focus on modeling, our interviews with 23 ML practitioners reveal that they improve model performance frequently by iterating on their data (e.g., collecting new data, adding labels) rather than their models. We also identify common types of data iterations and associated analysis tasks and challenges. To help attribute data iterations to model performance, we design a collection of interactive visualizations and integrate them into a prototype, Chameleon, that lets users compare data features, training/testing splits, and performance across data versions. We present two case studies where developers apply Chameleon to their own evolving datasets on production ML projects. Our interface helps them verify data collection efforts, find failure cases stretching across data versions, capture data processing changes that impacted performance, and identify opportunities for future data iterations.

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Designing Data: Proactive Data Collection and Iteration for Machine Learning

Lack of diversity in data collection has caused significant failures in machine learning (ML) applications. While ML developers perform post-collection interventions, these are time intensive and rarely comprehensive. Thus, new methods to track and manage data collection, iteration, and model training are necessary for evaluating whether datasets reflect real world variability. We present designing data, an iterative, bias mitigating approach to…
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CHI 2020

Apple had three papers accepted at the conference of Human-Computer Interaction (CHI), the premier international conference on interactive technology, in April 2020. Researchers from across the world gather at CHI to discuss, research, and design new ways for people to interact using technology. Although the conference was not held this year, you can read the accepted papers below.

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