Title:
Interactive Scalable Interfaces for Machine Learning Interpretability
Interactive Scalable Interfaces for Machine Learning Interpretability
| dc.contributor.advisor | Chau, Duen Horng | |
| dc.contributor.author | Hohman, Frederick | |
| dc.contributor.committeeMember | Zhang, Chao | |
| dc.contributor.committeeMember | Hodas, Nathan | |
| dc.contributor.committeeMember | Davidoff, Scott | |
| dc.contributor.committeeMember | Drucker, Steven M. | |
| dc.contributor.department | Computational Science and Engineering | |
| dc.date.accessioned | 2021-01-11T17:11:34Z | |
| dc.date.available | 2021-01-11T17:11:34Z | |
| dc.date.created | 2020-12 | |
| dc.date.issued | 2020-12-01 | |
| dc.date.submitted | December 2020 | |
| dc.date.updated | 2021-01-11T17:11:34Z | |
| dc.description.abstract | Data-driven paradigms now solve the world's hardest problems by automatically learning from data. Unfortunately, what is learned is often unknown to both the people who train the models and the people they impact. This has led to a rallying cry for machine learning interpretability. But how we enable interpretability? How do we scale up explanations for modern, complex models? And how can we best communicate them to people? Since machine learning now impacts people's daily lives, we answer these questions taking a human-centered perspective by designing and developing interactive interfaces that enable interpretability at scale and for everyone. This thesis focuses on: (1) Enabling machine learning interpretability: User research with practitioners guides the creation of our novel operationalization for interpretability, which helps tool builders design interactive systems for model and prediction explanations. We develop two such visualization systems, Gamut and TeleGam, which we deploy at Microsoft Research as a design probe to investigate the emerging practice of interpreting models. (2) Scaling deep learning interpretability: Our first-of-its-kind Interrogative Survey reveals critical yet understudied areas of deep learning interpretability research, such as the lack of higher-level explanations for neural networks. Through Summit, an interactive visualization system, we present the first scalable graph representation that summarizes and visualizes what features deep learning models learn and how those features interact to make predictions (e.g., InceptionNet trained on ImageNet with 1.2M+ images). (3) Communicating interpretability with interactive articles: We use interactive articles, a new medium on the web, to teach people about machine learning's capabilities and limitations, while developing a new interactive publishing initiative called the Parametric Press. From our success publishing interactive content at scale, we generalize and detail the affordances of Interactive Articles by connecting techniques used in practice and the theories and empirical evaluations put forth by diverse disciplines of research. This thesis contributes to information visualization, machine learning, and more importantly their intersection, including open-source interactive interfaces, scalable algorithms, and new, accessible communication paradigms. Our work is making significant impact in industry and society: our visualizations have been deployed and demoed at Microsoft and built into widely-used interpretability toolkits, our interactive articles have been read by 250,000+ people, and our interpretability research is supported by NASA. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/64147 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Machine learning interpretability | |
| dc.subject | Human-centered machine learning | |
| dc.subject | Explainable artificial intelligence | |
| dc.subject | Information visualization | |
| dc.subject | Visual analytics | |
| dc.subject | Human-computer interaction | |
| dc.subject | Interactive interfaces | |
| dc.subject | Machine learning | |
| dc.subject | Deep learning | |
| dc.subject | Neural networks | |
| dc.subject | Artificial intelligence | |
| dc.subject | Interactive articles | |
| dc.title | Interactive Scalable Interfaces for Machine Learning Interpretability | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Chau, Duen Horng | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | School of Computational Science and Engineering | |
| relation.isAdvisorOfPublication | fb5e00ae-9fb7-475d-8eac-50c48a46ea23 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isOrgUnitOfPublication | 01ab2ef1-c6da-49c9-be98-fbd1d840d2b1 | |
| thesis.degree.level | Doctoral |