Symphony: Composing Interactive Interfaces for Machine Learning

teaser

Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. 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. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. 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.

Citation

Symphony: Composing Interactive Interfaces for Machine Learning

Alex Bäuerle*, Ángel Alexander Cabrera*, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, Dominik Moritz
ACM Conference on Conference on Human Factors in Computing Systems (CHI). New Orleans, 2022.

BibTex

@inproceedings{symphony, author = {B\"{a}uerle, Alex and Cabrera, \'{A}ngel Alexander and Hohman, Fred and Maher, Megan and Koski, David and Suau, Xavier and Barik, Titus and Moritz, Dominik}, title = {Symphony: Composing Interactive Interfaces for Machine Learning}, year = {2022}, isbn = {9781450391573}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3491102.3502102}, doi = {10.1145/3491102.3502102}, booktitle = {CHI Conference on Human Factors in Computing Systems}, articleno = {210}, numpages = {14}, location = {New Orleans, LA, USA}, series = {CHI '22} }