J
Jina Suh
Researcher at Microsoft
Publications - 43
Citations - 2416
Jina Suh is an academic researcher from Microsoft. The author has contributed to research in topics: Classifier (UML) & Psychological intervention. The author has an hindex of 12, co-authored 37 publications receiving 1367 citations. Previous affiliations of Jina Suh include University of Washington.
Papers
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Proceedings ArticleDOI
Guidelines for Human-AI Interaction
Saleema Amershi,Daniel S. Weld,Mihaela Vorvoreanu,Adam Fourney,Besmira Nushi,Penny Collisson,Jina Suh,Shamsi T. Iqbal,Paul N. Bennett,Kori Inkpen,Jaime Teevan,Ruth Kikin-Gil,Eric Horvitz +12 more
TL;DR: This work proposes 18 generally applicable design guidelines for human-AI interaction that can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of human- AI interaction design principles.
Proceedings ArticleDOI
The Value of Semantic Parse Labeling for Knowledge Base Question Answering
TL;DR: The value of collecting semantic parse labels for knowledge base question answering is demonstrated and the largest semantic-parse labeled dataset to date is created and shared in order to advance research in question answering.
Proceedings ArticleDOI
ModelTracker: Redesigning Performance Analysis Tools for Machine Learning
TL;DR: ModelTracker is presented, an interactive visualization that subsumes information contained in numerous traditional summary statistics and graphs while displaying example-level performance and enabling direct error examination and debugging.
Journal ArticleDOI
Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers
TL;DR: Squares is presented, a performance visualization for multiclass classification problems that supports estimating common performance metrics while displaying instance-level distribution information necessary for helping practitioners prioritize efforts and access data.
Posted Content
Machine Teaching: A New Paradigm for Building Machine Learning Systems.
Patrice Y. Simard,Saleema Amershi,David Maxwell Chickering,Alicia Edelman Pelton,Soroush Ghorashi,Christopher Meek,Gonzalo Ramos,Jina Suh,Johan Verwey,Mo Wang,John Wernsing +10 more
TL;DR: This paper presents the position regarding the discipline of machine teaching and articulate fundamental machine teaching principles and describes how, by decoupling knowledge about machine learning algorithms from the process of teaching, this can accelerate innovation and empower millions of new uses for machine learning models.