EAGER: Interacting with Machine Learning through Visual Comparison of Outcomes

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This project will explore a new approach to the challenging problems of enabling people to work effectively with machine learning systems. The project will develop new tools that will assist users with a wide range of tasks, including building and tuning models, assessing and diagnosing them to build appropriate levels of trust, and using the learned models to gain insight on the data. In order to meet user needs in the face of increasingly large and complex systems, the project will develop a new approach to interacting with learning systems: providing interactive visualization tools that enable exploration and comparison in sets of model outputs. The project will explore tools for exploring learning experiment results, comparing these results with tests of other models, and helping run specialized tests. These new tools will have broad impact as they will enable a wide range of users to more effectively work with machine learning systems.This project will explore the viability of the outcome comparison approach by developing it in the context of two applications. In one, will develop tools for identifying causal insights from complex models. This will allow us to show that the approach scales to state-of-the-art model types and complex experiment designs. A second application will develop tools for diagnosing and interpreting embeddings from text corpora. This application will allow us to demonstrate the approach on complex data types and significant scales. The project is exploratory: there is no evidence that the approach can scale to such challenging scenarios, and the project must explore experiment strategies and visual designs that support large and complex scenarios. Products of the project, including publications, open source software, demonstrations and tutorials will be available from the project website.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.