Symphony: Composing Interactive Interfaces for Machine Learning

Alex Bäuerle

Ulm University

Ángel Alexander Cabrera

Carnegie Mellon University

Fred Hohman

Apple

Megan Maher

Apple

David Koski

Apple

Xavier Suau

Apple

Titus Barik

Apple

Dominik Moritz

Apple

Conference on Human Factors in Computing Systems 2022

Abstract

Interfaces for machine learning (ML) can help practitioners build robust and responsible ML systems. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.

Bibtex

@inproceedings{baeuerle2020symphony:,
	title={Symphony: Composing Interactive Interfaces for Machine Learning},
	author={B{\"a}uerle, Alex and Cabrera, Ángel Alexander and Hohman, Fred and Maher, Megan and Koski, David and Suau, Xavier and Barik, Titus and Moritz, Dominik},
	bookTitle={Proceedings of Conference on Human Factors in Computing Systems}
	year={2022}
}