Human-centered AI through scalable visual data analytics

Abstract

While artificial intelligence (AI) has led to major breakthroughs in many domains, understanding machine learning models remains a fundamental challenge. How can we make AI more accessible and interpretable, or more broadly, human-centered, so that people can easily understand and effectively use these complex models? My dissertation addresses these fundamental and practical challenges in AI through a human-centered approach, by creating novel data visualization tools that are scalable, interactive, and easy to learn and to use. With such tools, users can better understand models by visually exploring how large input datasets affect the models and their results. Specifically, my dissertation focuses on three interrelated parts: (1) Unified scalable interpretation: developing scalable visual analytics tools that help engineers interpret industry-scale deep learning models at both instance- and subset-level (e.g., ActiVis deployed by Facebook); (2) Data-driven model auditing: designing visual data exploration tools that support discovery of insights through exploration of data groups over different analytics stages, such as model comparison (e.g., MLCube) and fairness auditing (e.g., FairVis); and (3) Learning complex models by experimentation: building interactive tools that broaden people's access to learning complex deep learning models (e.g., GAN Lab) and browsing raw datasets (e.g., ETable). My research has made significant impact to society and industry. The ActiVis system for interpreting deep learning models has been deployed on Facebook's machine learning platform. The GAN Lab tool for learning GANs has been open-sourced in collaboration with Google, with its demo used by more than 70,000 people from over 160 countries.