Making Machine Learning Usable
Framework for applying ML explanations and augmentations for real-world decision making
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Framework for applying ML explanations and augmentations for real-world decision making
With the Sibyl libraries, you can augment machine learning applications for smoother human-ML interactions.
Sibyl has been demonstrated in the following application domains.
Child welfare
Explaining the predictions of child welfare predictive risk models, to aid the referral screening process
Check back here soon as our libraries go public!
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Alexandra Zytek, Dongyu Liu, Rhema Vaithianathan, Kalyan Veeramachaneni
TVCG IEEE Transactions on Visualization and Computer Graphics (VIS'21), 2022.
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni
TVCG IEEE Transactions on Visualization and Computer Graphics (VIS'21), 2022.
Download: [doi] [pdf] [bib] [video] [talk] | 🏆 Best Paper Honorable Mention (top 5% out of 440 submissions)
PhD (Project Lead)
DAI Lab, MIT
Postdoc (Project Co-Lead)
DAI Lab, MIT
Undergraduate Researcher
DAI Lab, MIT
Program Coordinator
DAI Lab, MIT
Principal Investigator
DAI Lab, MIT
Frontend Developer
DAI Lab, MIT
UI/UX Designer
DAI Lab, MIT
Animator & Graphic Designer
DAI Lab, MIT
This work is supported in part by NSF Award 1761812.
